Personal Identification Method using Footsteps

Some researches began to study the personal authentication using the footsteps which are used as the biological information. In general, the footstep identification methods are consist of a feature extraction method and a classification method. In the feature extraction method, the useful features, which are a power spectrum, a melcepstrum, a gait cycle, and so on, are extracted from audio signals of the footsteps. Using these features, the personal identification are performed by using Dynamic TimeWarping (DTW), k-means clustering method, and Support Vector Machine (SVM) in the classification method. However, it has not been established the suitable methods for the personal identification using the footsteps. Hence, we investigate the feature extraction method and the classification method. In this paper, we propose an identification method which uses Mel-Frequency Cepstrum Coefficients (MFCCs) as footstep features, k-Nearest Neighbor (k-NN) which uses DTW as a distance measure and Gaussian Mixture Models (GMMs) as classifiers.


Variability in EEG with Single Point Sensing as Inter-Individual Difference Measure Using Self-Organizing Map

Proc. of 2011 International Symposium on Nolinear Theory and its Applications, NOLTA2011

In this paper, we introduce an EEG analysis technique to confirm an inter-individual difference in prefrontal cortex EEG with a single point sensing. The device for recording the EEG uses the dry-type sensor and a few numbers of electrodes. The EEG analysis adapts the feature mining on EEG pattern using a self-organizing map (SOM). The EEG patterns are determined based on the preference evaluation on sound listened to. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are theta, low-alpha, high-alpha, low-beta, high-beta, respectively. To confirm the inter-individual difference, we do experiments using real EEG data. These results show that the learning results by SOM on each human are clearly different when using same initial weight values for the SOM.


Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations

Proc. of The IEEE International Conference on Information Reuse and Integration (IRI2011)

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.


Supervised Feature Extraction Algorithm by Iterative Calculations

Proc. of The 2nd Conference on Next Generation Information Technology (ICNIT2011)


Feature Selection Method for Music Mood Score Detection

In general, music retrieval and classification methods using music moods use a lot of acoustic features similar to music genre classification. These features are used as the spectral features, the rhythm features, the harmony features, and so on. In this paper, we propose a feature selection method for detecting music mood scores. In the proposed method, features which have strong correlation with mood scores are selected from a lot of features. Then, these are input into Multi-Layer Neural Networks (MLNNs) and mood scores are detected every mood labels. For evaluating the proposed method, we conducted the music mood score detection experiments. Experimental results show that the proposed method improves the detection performance compared to not use the feature selection.


Operation Improvement of Indoor Robot by Gesture Recognition

Recently, the demand for the indoor robots has increased. Therefore, increased opportunities for many people to operate the robots have emerged. However, for many people, it is often difficult to operate a robot using the conventional methods like remote control. To solve this problem, we propose a robot operation system using the hand gesture recognition. Our method pays attention to the direction and movement of the hand. We were able to recognize several gestures in real-time.


Background Updating for Abandoned Luggage Detection at Train Stations


Novel Approximate Stastical Learning Algorithm for Large Complex Datasets

In pattern recognition, simple Principal Component Analysis (SPCA) method is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional image datasets. However, when input data are distributed in a complex way, SPCA which is a linearly feature extraction method, might not be efficient. In such cases, kernel method, which is nonlinear feature extraction method, was shown its high generalization capability. In this paper, we apply kernel method to SPCA, and show the derivation. Finally, it was tested and compared with original SPCA algorithm by using Japanese paper currency images.


A Simple Interface for Mobile Robot Using Motion Stereo Vision

In remote control, user interfaces are very important and it is desired that they have intuitive operability and be simple systems. However, presently, most of interfaces have not satisfied these needs. Therefore, we propose a simple interface to control mobile robots equipped with a camera by instructing it to move to a desired location using the image set to a screen. For the robot to control its movement, it needs to calculate the three-dimensional information of the instructed point. In this work, we calculate the three-dimensional position using motion stereo vision, and perform experiments using a wheeled mobile robot with a single camera in a real environment.


Improving mobility for blind persons using video sunglasses

Proceedings of 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision

Blind people navigate safely through a familiar room based on strong expectations about the location of objects. If something has been moved, added or removed, it can present a difficulty and potentially a danger. Human eyes are one of the most important body parts that help humans to understand and interact with their surroundings. Most learning and recognition of objects around us is accomplished using the eyes. Given the recent advancement of imagery systems and ever increasing processing power of microprocessors, a machine vision aiding system for the blind can be a reality. In the initial system we propose a system consisting of camera equipped sunglasses to capture the images and pattern recognition to automatically detect Braille tiles to aid mobility of blind persons. The information obtained is passed to the subject using audio messages.


Robust Age Estimation for Face Posture Change

the 8th edition of France-Japan and 6th Europe-Asia Congress on Mechatronic 2010


Extraction of Brain Regions for Image Diagnosis of Alzheimer-type Dementia Based on Atrophy Progress Speeds

Conference Record of 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference, M19-375

In this paper, we propose an extraction method of the ROI fitting to individual brain shape and size using two deformable models hierarchically. Moreover, we discuss diagnostic application using atrophy progress speeds briefly.


Invisible Print-type Calibration Pattern based on Human Visual Perception

Proceeding of Int. Conf. on Image Processing'2010

In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on human visual perception. We embed the calibration pattern in an original image by adding high frequency component to blue intensity in a limited region. Moreover, the proposed calibration pattern protects page layout and artwork.


Investigation of Accuracy for drawing of Object Detection Method with Sketch

Proc. of World Automation Congress (WAC'2010)

There are many image retrieval systems. In these systems, it is difficult to search an object which can not describe in words. Moreover, tagging with words and prior training are necessary. To solve these problems, a purpose of our study is a intuitive and visual image retrieval system using a sketch. As a basis of the system, object detection method with sketch have been proposed. In this paper, an effect of the difference of various sketches in the proposed method is investigated by an experiment. As a results, it is shown that the proposed method is influenced by a detailed part of the sketch, and too sensitive detection is achieved.


Improvement of Likelyhood in Particle Filter for Interactive Color Tracking

In this paper, we propose an idea that is improving method of likelihood in particle filter. Following methods have been proposed as the previous method by us. First, an user is able to select a tracking object as a region of interest (ROI) in a single image from video sequence. Then, color feature in the ROI are used as a color of interest (COI). During the tracking, two color components of HSV color space are automatically chosen by the color feature analysis in each particle. However, in the previous method, values of likelihood is fixed. If values of particles are in range of the COI, the likelihood value is 1.0. Otherwise particles are set in 0.2 as the likelihood value. This value is fixed regardless of the distance from COI. To improve accuracy of tracking, we propose the method that the likelihood is dynamically changed depending on the distance from COI. In our experiments, the proposed method is compared with the previous method. As results, the accuracy of the proposed method is better than the previous method.


Classification of Hand Postures Based on 3D Vision Model for Human-Robot Interaction

Proc. of 19th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man'10)

In this paper, a method for hand posture recognition, which is robust for hand posture changing in an actual environment, is proposed. Conventionally, a data glove device and a 3D scanner have been used for the feature extraction of hand shape. However, the performance of each approach is affected by hand posture changing. Therefore, this paper proposes the posture fluctuation model for efficient hand posture recognition, based on 3D hand shape and color feature obtained from a stereo camera. A large set of dictionary for posture recognition is built by various leaned hand images which were auto-created from one scanned hand image, based on plural proposed models. In order to show the effectiveness of proposed method, performance and processing times for posture recognition are compared to conventional method. In addition, we perform the evaluation experiment by using the Japanese sign language.


Association between Ego Scores and Individual Characteristics in EEG Analysis --- Basic Study on Individual Brain Activity

This paper introduces a method for discussing an association between ego scores and individual characteristics in EEG analysis. The ego scores express one of the personality and/or nature on a human. The EEG analysis calculates the power spectra of the frequency weighted of the EEG signal, and evaluates through detecting the EEG patterns while listening to the music. A k-nearest neighbor classifier is used to detect the EEG patterns. The specified weight value to weight frequency of EEG and the results of EEG pattern detection express the individual characteristics in EEG analysis. Finally, we do experiment using a real EEG data for discussing the association the ego scores and the individual characteristics in EEG analysis. An interesting tendency was that the subjects who were the combined ego-type tended to have a powerful response to negative stimuli more than positive stimuli. They had the stable filter weighted frequency.


Music Impression Detection Method for User Independent Music Retrieval System

In previous work, we have proposed the automatic sensitive word score detection system for a user dependent music retrieval system. However, the user dependent method causes a lot of burdens to the user because the system requires a lot of data for adapting it to each user. Hence, in this paper, we propose an automatic sensitive word score detection method for a user independent music retrieval system and evaluate the proposed method using 225 music data. Experimental results show that 87.5% of music patterns succeeded in detection of sensitive word score in the case that the difference between estimated and evaluated score is 1 (Error 1 rate). Moreover, we conduct subjective evaluation experiments to evaluate the proposed method as a utility method. From this experiment, it is observed that the user satisfaction level of the proposed method is higher than random selection impression detection.


Age Estimaton Using Kernel Regression Analysis

Proceeding of International Symposium on Nonlinear Theory and its Applications (NOLTA2010)


An Embedding and Detection Method of Invisible Calibration Pattern for Print-Type Data Hiding


Invisible Calibration Pattern based on Human Visual Perception Characteristics

Proceeding of Int. Conf. on Pattern Recognition'2010

In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points corresponding to between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on characteristics of human visual perception. The calibration pattern is embedded to blue intensity in an original image by adding high frequency component.


Recognition of Abnormal Vibrational Responses of Signposts using the Two-dimensional Geometric Distance and Wilcoxon Test

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering

In expressway companies, workers have been impacting signposts using wooden hammers and estimating the degree of the corrosion by listening to the sound. In order to automate this, we have been developing software that recognizes an abnormal impact vibrational response due to corrosion. This software extracts sonograms from impact vibrational waves using the LPC spectrum analysis, and matches images of the sonogram between a standard and an input impact vibrations using the Two-dimensional Geometric Distance. Then, the software distinguishes the abnormality of the input impact vibration using Wilcoxon rank-sum test. We have measured the impact vibrations of five normal signposts and five abnormal signposts, and carried out the automatic recognition experiments. As a result, the software has recognized correctly in all cases. We have verified the effectiveness of the proposed method.


Study on Association between User's Personality and Individual Characteristic of Left Prefrontal Pole EEG Activity

Proc. of 2010 Sixth International Conference on Natural Computation (ICNC'10)

This paper introduces a method for discussing the association between human personality based on egogram scores and the results of classifying the electroencephalogram (EEG) patterns while listening to the music. The egogram based on psychological testing is used for analyzing his/her personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified using the k-nearest neighbor method. Finally, we discuss the association between his/her personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection accuracy of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme.


Study on Relationship between Personality and Individual Characteristic of EEG for Personalized BCI

It is an important issue that the relationship between personality and individual characteristic of an electroencephalogram (EEG) for using a personalized brain computer interface on daily basis. In this paper, we introduce a method for discussing the relationship between personality based on egogram scores and the results of the EEG pattern detection. The egogram based on psychological testing is used for analyzing human personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified/detected using the k-nearest neighbor method. Finally, we discuss the relationship between human personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection ratio of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme.


Age and Gender Estimation by using Facial Image

In this paper, we propose age and gender estimation system by vairous features. Age and gender has a lot of characteristics. These characteristics are one of the difficult cognitive process in human interaction. If we can extract the important feature of this cognitive process, it is considered that the age and gender estimation by the machine becomes possible. Therefore, we propose a method of age and gender feature extraction and estimation using the face image. In this paper, the age of the face means apparent-age that is based on the human perception of age. Moreover, person's aging and gender difference appear in the faces. For example, the pigmented spot, the wrinkle, sagging skin, shape, color of skin, and so on. Thus, we extract these several features for age and gender estimation. Furthermore, we estimate a continuous age and gender using a neural network (NN).


A Proposal of Feature Extraction for Impression Analysis

The concern of image processing has increased, in recent year. However, the evaluation of facial impression that we are naturally doing from ones face in our daily life is not treated in this area. The automatic facial impression evaluation applies for wide area. Then we analyzed important facial featres for impression analysis, in order to achieve automatic evaluation of facial impression. In order to evaluate the proposed method, we performed computer simulation. As the result of this simulation, we confirmed that important facial features for facial impression are difference while gender and age.


Invisible Calibration Pattern Based on Human Visual Perception

In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on human visual perception. We embed the calibration pattern in an original image by adding high frequency component to blue intensity in a limited region. Moreover, the proposed calibration pattern protects page layout and artwork.


Driver Alcohol Consumption Detection System using EEG

Proceeding of 2010 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'10


Biped Robot Walking on Uneven Surfaces using Reinforcement Learning

Recently, reinforcement learning is one of the approaches focused on to achieve adaptive motion in unknown environment. However, most existing approaches require a large number of trials, which becomes one the major problems when the method is applied to an actual robot. Therefore, in this study, we propose an approach that attains low calculation cost to adapt the robot to unknown environments using reinforcement learning. To increase learning accuracy and efficiency, three patterns of rewards are tried and compared for verification. As a result, 70% success rate walking on small uneven surface was achieved


A Simple Interface for Mobile Robot using Motion Stereo Vision

Recently, various kinds of robots are present close to our living environment from their original limited usage in factories, etc. Especially, remote-controlled robots with mobile actuator are being put to practical use for some purposes such as conveying systems, home and office security etc, because the use of remote-controlled robots is easier than autonomous robots. These mobile robots require a controller. In this work, we develop a simple interface for mobile robots. To uses this interface, the users need only point to the position on a screen where they want the robot to move to. After that, the robot controls its own movement between the current position and the target position. To autonomously move to the selected point, the robot needs to calculate the direction and distance of the target from its own position. To calculate direction, we use a simple camera model and pixels surrounding the target point to extract a template. Template Matching is then used to recognize the point during movement. Our interface can forecast movement zone of template image, because it controls movement of the robot. To calculate distance, we use Motion Stereo Vision (MSV). We have two reasons for using this method. First, MSV needs only one camera therefore cost is reduced, and secondly, MSV involves camera movement which is well suited for the mobile robot we are constructing.


Hand Gesture Recognition under Variable Pose Based on 3-D Appearance Model


Change of the important impression part in the face according to the age

The achievement of the automatic impression evaluation can apply to wide area application such as automatic generation of CG, automatic likeness making in a criminal investigation, cosmic sergeries, and entertainments. However the impression evaluation that we are naturally doing in daily life is not treated conventional researches. Then we stuy the relationship of the change between facial impression and facial part by age in this paper.


Speaker vector combination method of air- and bone-condction speech for speaker identification

Recently, some new sensors, such as bone-conductive microphones, throat microphones, and non-audible (NAM) microphones, besides conventional condenser microphones have been developed for collecting speech data. We focus on boneconduction speech data collected by the bone-conductive microphone. Generally, a Gaussian Mixture Models (GMMs) are used for the speaker recognition. However, recognition performance of these models is low under the condition that the number of enrollment data is small. Hence, the speaker recognition method using speaker vectors has been proposed. It is reported that this method is useful for the speaker recognition when the number of enrollment data is small. Accordingly, in this paper, we propose a speaker vector combination method of using air- and bone- conduction speech for the speaker identification. The proposed method individually trains the speaker vector of air-conduction speech and the speaker vector of bone-conduction speech. After that the proposed method combines these speaker vectors and uses these for speaker identification. For evaluating the proposed method, we conducted 100 female speakers speaker identification experiment. Experiment results show that the speaker identification accuracy of the proposed method is able to improve that of each speech. In fact, 60.1% of the accuracy of the proposed method has improved 53.3% of air-conduction speech.


Music Classification using Sensitive Words

In this paper, we propose an automatic sensitive word score detection method for a music retrieval system. The proposed method consists of two parts, which are a feature extraction part and a score detection part. The score detection part detects a sensitive word score of each music. In this paper, we use an impression represented by 7 levels for the sensitive word score and the Multi-Layer Neural Networks (MLNNs) are used for detector of sensitive word score. Moreover, we investigate the effective features to detect the sensitive word score. To evaluate the proposed method, we conduct a sensitive word score detection experiment.


Improved Feature Generation Ppoperty in Fast Statistical Learning Algorithm

This paper presents a new accuracy improvement technique in the statistical learning algorithm Simple-FLDA (Simple-Fisher Linear Discriminant Analysis) which was presented by authors. Recently, iterative learning algorithms for obtaining eigenvectors in the principal component analysis (PCA) have been presented in the field of pattern recognition and neural network. On the one hand, the Fisher linear discriminant analysis (FLDA) has been used in many fields, including especially face image analysis, and compared to PCA in relation to feature generation. However there are drawbacks of FLDA such as a long computational time based on a large-sized covariance matrix, the limit of the number of effective eigenvectors, and the issue that the within-class covariance matrix is usually singular. In order to overcome this difficulty, we proposed the feature generation method Simple-FLDA which is approximately derived from geometrical interpretation of FLDA. This algorithm is similar to the Simple-PCA and does not need matrix operation. In this paper, a new calculation technique of eigenvectors in the Simple-FLDA is presented. Its preliminary simulation results are given for a simple face recognition problem and others.


Detection of Abandoned Objects from Fluctuating Environment Using Active Background Subtraction

This paper proposes a new method to detect abandoned objects with a focus on Active Background Subtraction. Some estimate background models for dealing with fluctuating environment are utilized. These models are expressed in the HSV color model for more easily removable shadows and adaptive illumination changes. Using these models for background subtraction the object areas from a background image can be divided. In addition, some edge images that are generated from an input image and the value of difference between background models and the input image are used. This method can fix the object area size and rule out shadows as a candidate for abandoned objects. Finally we make a decision regarding the abandoned objects on the basis of displacement and presence time. Preliminary experiments show that the present method is effective for detecting abandoned objects.


Improvement Algorithm for Approximate Incremental Learning

This paper presents an improved algorithm of Incremental Simple-PCA. The Incremental Simple-PCA is a fast incremental learning algorithm based on Simple-PCA. This algorithm need not hold all training samples because it enables update of an eigenvector according to incremental samples. Moreover, this algorithm has an advantage that it can calculate the eigenvector at high-speed because matrix calculation is not needed. However, it had a problem in convergence performance of the eigenvector. Thus, in this paper, we try the improvement of this algorithm from the aspect of convergence performance. We performed computer simulations using UCI datasets to verify the effectiveness of the proposed algorithm. As a result, its availability was confirmed from the standpoint of recognition accuracy and convergence performance of the eigenvector compared with the Incremental Simple-PCA.


Palm print Recognition Based on Local DCT Feature Extraction

In this paper we present a method which extracts features from palmprint images by applying the Discrete Cosine Transform (DCT) on small blocks of the segmented region of interest consisting of the middle palm area. The region is extracted after careful preprocessing to normalize for position and illumination. This method takes advantage of the well known capability of the DCT to represent natural images using only a few coefficients by performing the DCT on each block. After ranking the coefficients by magnitude and selecting only the most prominent, these are then concatenated into a compact feature vector that represents each palmprint. Recognition and verification experiments using the PolyU Palmprint Database show that this is an effective and efficient approach, with a recognition rate above 99 % and Equal Error Rate (EER) of less than 3 %.


Speaker verification method using bone-conduction and air-conduction speech

骨道音と気道音を用いた話者認証法

Proceedinngs of 2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2009)

Recently, some new sensors, such as bone-conductive microphones, throat microphones, and non-audible murmur (NAM) microphones, besides conventional condenser microphones have been developed for collecting speech data. Accordingly, some researchers began to study speaker and speech recognition using speech data collected by these new sensors. We focus on bone-conduction speech data collected by the bone-conductive microphone. In this paper, we first investigate speaker verification performances of bone-conduction speech. In addition, we propose a method of using bone-conduction speech and air-conduction together for the speaker verification. The proposed method integrates the similarity calculated by air-conduction speech model and similarity calculated by bone-conduction speech model. Using 99 female speakers' speech data, we conducted speaker verification experiments. Experimental results show that the speaker verification performance of bone-conduction is lower than that of air-conduction speech. However, the proposed method can improve the speaker verification performance of bone- and air-conduction speech. Actually, the proposed method can reduce the equal error rate of air-conduction speech by 16.0% and the equal error rate of bone-conduction speech by 71.7%.

近年，骨伝導マイク，声帯マウス，非可聴つぶやき声(NAM)マイクなどの新しいセンサが開発された．また，これらのマイクを使った研究も行われるようになった．本国際会議論文では骨伝導マイク収集された骨伝導音声データに焦点を当てる．最初，骨伝導音声の話者認証実験を行う．次に，骨伝導音と気道音の両方の音声を用いた方法を提案する．99名の女性話者の音声データを用いて話者認証実験を行い，骨伝導音の結果は気道音より悪いことが分かった．しかし，両方の音を併用することで話者認証精度を改善できることが判った．


Wrist EMG Signals Identification using Neural Network

Recently, researches of artificial arms and pointing devices using electromyogram (EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.


A Study on Relationship between Personal Feature of EEG and Human's Characteristic for BCI Based on Mental State

This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the humans nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A k-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the humans nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.


Out-of-Focus Blur Image Restoration using the Akamatsu Transform

In this paper, a new image restoration approach using the Akamatsu transform is presented. In the method, by using repeated simple calculations based on the Akamatsu transform, an out-of-focus blurred image can be restored and sharpened. Out of focus is a major problem bothering many people in photography, especially the amateurs. There exits some solution to this problem on both the hardware and software sides. However, none offers a perfect solution. The Akamatsu transform can easily be embedded into hardware to offer faster processing due to its simplicity. Although, initially proposed for speech processing, this paper show the effectiveness of the transform in image processing. The Akamatsu transform is a combination of integral and differential transforms. The algorithm can be effective tools for image restoration in realtime image processing.


Face Detection Using RBF Network and particle Filter for AIBO

Proc. of the 41st ISCIE International Symposium on Stochastic Systems Theory and Its Applications


An Analysis of Influence of Facial Feature for Apparent Age Estimation

The purpose of this paper is to analyze apparent age feature based on age perception. In order to construct the apparent age database, age estimation experiment is performed to the HOIP facial database by 60 subjects. We defined 46 age features which contribute to the age perception from facial image. This process is performed based on research on age acknowledgment in field psychology and technological conventional method about real-age estimation from facial image. The apparent age feature used potentially when human performs age estimation is decided from 46 defined features by using the AIC and the multiple linear regression analysis. By using the proposed method, facial features that influenced on the apparent age in each gender was confirmed.


An Important Facial Parts Analysis for Evaluating the Impression - Difference by Gender

The concern for image processing has increased very much, in recent year. However, the evaluation of facial impression is not treated in this area that we are naturally doing in our daily life to see on one's face. Wide ranges of applications are released from the achievement of the impression automatic evaluation. Then we analyze important facial parts for impression analysis, in order to achieve automatic evaluation of facial impression. In order to analyze the important facial parts, we performed computer simulation. As the result of this simulation, difference of important areas by gender that relate impression words were detected.


Age and Gender Estimation System Based on Human Perception

In this paper, we propose age and gender estimation system by vairous features. Age and gender has a lot of characteristics. These characteristics are one of the difficult cognitive process in human interaction. If we can extract the important feature of this cognitive process, it is considered that the age and gender estimation by the machine becomes possible. Therefore, we propose a method of age and gender feature extraction and estimation using the face image. In this paper, the age of the face means apparent-age that is based on the human perception of age. Moreover, person's aging and gender difference appear in the faces. For example, the pigmented spot, the wrinkle, sagging skin, shape, color of skin, and so on. Thus, we extract these several features for age and gender estimation. Furthermore, we estimate a continuous age and gender using a neural network (NN).


Driver Dozing Detection System Using the Near-Infrared Camera Images

Recently, the research to handle face information with computer is being done. Therefore, how to search the face area automatically is very important. For this problem, there are many problems to get the faces. However these researches are for color image. There are few researches using the near-infrared camera. The purpose of this research is to recognize a face with a near-infrared camera. The face detection that used images from near-infrared camera is comparatively difficult to be done, because they are gray scale images. In this paper, the filter by using GA is designed, and the method of detecting the face and the position from the near-infrared images is proposed. It is demonstrate that our approach is effective for vehicle driver monitoring.


Biped Robot Walking Control using Image Processing

In this paper, a vision system for controlling an autonomous biped robot is presented. Robots need sensors to understand circumstances. There are several types of sensors. Recently, a camera is rapidly becoming the sensor of choice due to the remarkable improvement in computer technology especially in processing power. However, there is the blurred image problem because of camera shake due to robot's walking. Our method focuses on the use of image processing to control a humanoid robot's walking. We fix a camera on a biped robot, and input the images from the camera to PC for processing. The results of the image processing are sent to the robot for control immediately. These real-time image processing and control systems give us an efficient application on future robots. In the initial experiment the objective is to control robot walking upstairs.


A Circle-Based Region-Of-Interest Segmentation Method for Palmprint Recognition

This paper presents a novel method for optimal region-of-interest (ROI) segmentation for palmprint feature extraction based on the largest inscribed circle. This ensures that the optimal amount of features can be extracted, unlike square-based methods which exclude a substantial area on the outside region of the palmprint image. After position normalization, the middle portion of the palmprint is searched to determine the center from which the largest inscribed circle can be extracted. The circular area is then unwrapped into a fixed-size rectangular strip which is further preprocessed to remove redundancies and then split into seven equal square sub-images. A layered approach is then adopted during the matching stage where each square is successively matched and polled to produce a matching score. Experiments are performed using the `PolyU Palmprint Database' and results show that this is a viable method for palmprint feature extraction, with a recognition rate of above 90% obtained.


Identification of Wrist EMG Signals using Dry Type Electrodes

Recently, researches of artificial arms and pointing devices using electromyogram(EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. However, when we use the wrist EMG, there are problems that the individual difference is large and its repeatability is low and so on. In this paper, we aim the construction of wrist EMG recognition system that is robust to these problems.


Automatic Keyword Additional System by Using the GA anfd the Fuzzy Analysis


Analysis of Important Facial part for Facial Impression Analysis


An Apparent-Age Estimation System Based on Several features


Improvement of Evolutionary Eye Sensing with Buccal Region",


Relation Between Mental Change and EEG When Performing Trivial Tasks

Recently, due to long time usage of computers and Visual Display Terminals (VDTs), mental stress and related illnesses have increased tremendously in today's contemporary society. If a computer based method to diagnose stress becames available, it would go a long way in reducing stress illnesses. Hence, it is thought that quantification of stress is important. In this paper, we study the relationship between stress and electroencephalogram (EEG) signals. We studied the effect of stress (the pysiological parameter) when the brain is doing trivial tasks by measuring the brain waves of a subject before and after doing such a task. We apply principal component analysis (PCA) and linear discriminant analysis (LDA) to characterize the change in each component, extract it and discriminate using a neural network (NN). The trivial task used in this experiment is asking the subject to pick and move beans from one plate to another using chopstick. As a result, it was found that PCA is an effective analysis method in this research. Therefore, it can be said that even trivial task have an effect on the brain that may contribute to stress.


Non-Stationary Noise Robust Speech Recognition Method using Repetitive Phrase

In this paper, we propose a novel noise-robust speech recognition technique using a repetitive phrase. The repetitive phrase means that the same speech command word is repeatedly uttered. Since there is few repetitive phrase with many syllables in daily conversation, it is consider that a incorrect acceptance of speech commands can be reduced. Moreover, we expect that the non-stationary noise does not influence to the same parts of each phrases in repetitive phrase. Hence, it is able to improve speech recognition performance because we can create a clean speech command by using the repetitive phrase. In this paper, we propse the speech recognition method which combines two-lattices. We conducted the speech recognition experiment using three kinds of noise: a buzzer, a whistle, and a telephone ring. From the experimental results, the proposed method improved the speech recognition accuracy under all noise conditions compared to speech recognition without repetitive phrase. Especially, the proposed method improved the speech recognition accuracy from 21% to 63% under the phone noise condition.


Classification of the vision correction by the EEG


A Basic Method for Classifying Humans Based on an EEG Analysis

This paper introduces a method for extracting and confirming a human feature by analyzing prefrontal cortex electroencephalogram (EEG) activities on listening to music that user feels matching, no matching his mood and otherwise. The proposed method is EEG sensing and analyzing to monitor the human feature and classify the human with the activity of different frequency bands of the power spectrum of the EEG signal, which is simple and basic. Finally, the performance of the proposed method is evaluated using real EEG data. We confirm to be able to classify the human into three or more groups.


Incremental Learning Method for Biological Signal Identification

Proc. of 13th International Conference on Biomedical Engineering (ICBME2008)


Fast statistical learning with Kerne-Based Simple-FDA

Proc. of IEEE International Conference on Signal Image Technology and Internet Based Systems

In this paper, new statistical learning algorithms with kernel function are presented. Recently, iterative learning algorithms for obtaining eigenvectors in the principal component analysis (PCA) have been presented in the field of pattern recognition and neural network. However, the Fisher linear discriminant analysis (FLDA) has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome this difficulty, we proposed the feature generation method simple-FLDA which is approximately derived from geometrical interpretation of FLDA. This algorithm is similar to simple-PCA and does not need matrix operation. In this paper, new statistical kernel based learning algorithms are presented. They are extended versions of simple-PCA and simple-FLDA to nonlinear space using the kernel function. Their preliminary simulation results are given for a simple face recognition problem.


Specific speakers' speech corpus over long and short time periods


Interactive Interface Using Evolutionary Eye Sensing

Proceedings of the 11th IASTED International Conference Intelligent Systems and Control (ISC 2008)


Classification of Fingerprint Images into Individual Classes Using Neural Networks

Proceedings of the 34th Annual Conference of the IEEE Industrial Electronics Society


The Music Analysis Method Based on Melody Analysis

Recently, we can have large amounts of music thanks to the development of the computer technology. However, as the data of the music becomes larger, it is a hassle to classify the music based on the music content manually. We think that it is necessary to categorize the music automatically based on our mood. In this paper, we propose a novel method to analyze the music automatically based on melody analysis. The proposed method considers music genres as the measure of music analysis. We extract the acoustic features to characterize the music. Then, we classify music using multi-class classifiers based on the support vector machine (SVM).We adopt two approaches to the multi-class classification method. Furthermore, we propose a visualization method to specify the musical structure on the music genres from the classification result. Finally, computer simulations are done by using real music data in order to prove the effectiveness of the proposed method.


In this paper, we propose an age estimation system on the AIBO. AIBO is an entertainment robot produced by SONY co., Ltd.. AIBO has many sensors to get information around itself and moves according to its instinct. This autonomous action is considerably natural. However, it is inadequate to communicate with people. If AIBO can estimate the human age from a face image, it becomes more excellent entertainment robot. Then, we propose the age estimation method on the AIBO by using face image. In this paper, the apparent age feature is extracted by the fast Fourier transform (FFT), and it is selected by the GA. Moreover, the age is estimated by the 1-dimensional SOM. In order to show the effectiveness of the proposed method, we show the simulation examples.


Combination method of Bone-conduction Speech and Air-conduction Speech for Speaker Recognition


The Extraction of the Personal Coloration Pattern for Color Design System


A Method for Filtering Frequency of EEG using Real-Coded Genetic Algorithm

This paper proposes a method for filtering frequencies of an electroencephalogram (EEG) on EEG analysis with a real-coded genetic algorithm (RGA). An EEG of humans has the different personal feature that contains an individual error and a change of his/her physical and mental conditions at the occasion arises. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weighted value on frequencies. These weighted values are through to express personal feature of EEG signal. The proposed method calculates the power spectra of the frequency of the EEG signal, is weighted these frequencies, divides the frequency bands based on theta, alpha1-3, and beta rhythms, and evaluates whether the music matches mood of the user by EEG pattern classification. A real-coded genetic algorithm is used to specify the weighted value of frequency of the EEG. A k-nearest neighbor classifier is used to classify the EEG patterns. Finally, the performance of the proposed method is evaluated using real EEG data by classifying EEG patterns that listen to music matching, no matching mood of the subject, and feeling otherwise.


Feature Extraction System for Age Estimation


Time-series Models of the EEG Wearing Overcorrected Glasses

多くの眼鏡ユーザは，過度に補正された眼鏡を使用している場合が少なくない．過度に補正された眼鏡の使用は眼に影響を与える．しかしながら，その影響を測り，評価する方法が確立されていない．そこで，脳波を分析することで，過度の補正が実施されている状態を評価する手法を提案する．脳波で過度の補正状態を分析するために，実験を実施した．実験では，PCゲームを30分間実施しているときの脳波を計測する．過度の補正状態と適正補正状態において，脳波の変化が異なることを確認した．


Incremental Algorithm of Discriminant Analysis

This paper presents a new incremental learning algorithm named Incremental Simple-FLDA. It is the algorithm that improved Simple-FLDA to update an eigenvector by using incremental data. Simpe-FLDA is an approximation algorithm of the linear discriminant analysis where an eigenvector can be calculated by a simple repeated calculation. Using this proposal algorithm, the eigenvector is approximately obtained whenever incremental data is added one by one. We carry out computer simulations on personal authentication that uses face images by incremental learning to verify the effectiveness of this algorithm. As a result, the effectiveness of proposal algorithm is shown.


Incremental Learning Method of Simple-PCA

n this paper, we propose an incremental learning algorithm named Incremental Simple-PCA. This algorithm is added an incremental learning function to the Simple-PCA that is an approximation algorithm of the principal component analysis where an eigenvector can be calculated by a simple repeated calculation. Using the proposed algorithm, it allows to update faster the eigenvector by using incremental data. To verify the effectiveness of this algorithm, we carry out computer simulations on personal authentication that uses face images and wrist motion discrimination using wrist EMG by incremental learning. As a result, we can confirm the effectiveness from the aspects of accuracy and a computing time by comparing the Incremental PCA that gave the incremental learning function to the conventional PCA.


Improving the Robustness of Lips Sensing with Evolutionary Video Processing

In this paper, an effective method is proposed for robust lips sensing. Our objectives are high-speed lips tracking and data acquisition of a talking person in natural scenes. Our approach is based on the Evolutionary Video Processing. This method has a trade-off between accuracy and a processing time. To solve this problem, we proposed automatic Search Domain Control method and implement this method in the Evolutionary Video Processing. The tracking accuracy is improved from 66.3% to 84.9%. The proposed method can recover from occlusion and tracking loss. Comparative experiments are presented to demonstrate the effectiveness and robustness of the proposed method.


Facial Gesture Simulation for a Single Image using Image Warping


Identification of Music According to Singer

We propose a singer identification method using a singer model for retrieving the music data. For the speaker models, we use Gaussian Mixture Models (GMMs) and Vector Quantization (VQ) centroids. To evaluate the proposed method, we conducted the speaker identification experiments. Experimental results show that the correct identification accuracy is 65.0% under the condition that the GMMs are used for the speaker models and the 81.9% under the condition that the VQ centorids are used for the speaker models. We also experimented changing the training data to estimate the best parameter for GMM. The correct identification accuracy is 50.0%, under the condition that training data is 1 and the 95.0%, under the condition that training data is 8. Owing to result of GMM, it has been understood that accurate parameter estimation becomes possible by increasing the number of training data.


Relation between mental change and EEG when doing Trivial Tasks

Recently, due to long time usago of computers and Visual Display Terminals (VDTs), mental stress and related illnesses has increased tremendously in today's contemporary society. If a computer based method to diagnose stress were available it would go a long way in reducing stress illnesses. Hence, it is thought that quantification of stress is important. In this research, we study the relationship between stress and electroencephalogram (EEG) signals. We studied the effect of stress (the pysiological parameter) when the brain is doing trivial tasks by measuring the brain waves of a subject before and after doing such a task. We then apply simple principal component analysis (Simple-PCA) to characterize the change in each component, extract it and discriminate using a Neural Network (NN). The trivial task used in this experiment is asking the subject to pick and move beans from one plate to another using chopstick. A recognition accuracy of about 80 % was achieved.


Face Information Processing by Fast Statistical Learning Algorithm

In this paper , we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA. First of all, the approximated principal component analysis (learning by Simple-PCA) that uses the mean vector of each class is calculated. Next, in order to adjust within-class variance in each class to 0, the vectors in the class are removed. By this processing, it becomes high-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images.


Importance analysis of face part in face impression


Feature Generation for Face Recognition by Fast Statistical Learning Algorithm

In this paper,we proposed a new statistical learning algorithm. This paper quantitatively verifies the effectiveness of the feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher Linear DIscriminant Analysis. As a high-speed feature extraction method, this algorithm is improved version of Simple-FLDA. First of all, the approximated proncipal component analysis (learing by Simple-PCA) that uses the mean vector of each class is calculated. Next, to adjuast within-class variance in the class to 0, the vectors in the class are removed. By this processing, it becomes hi-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images.


An Analysis of the Influence of Facial Feature on Age Perception


Enhancement of Palmprint Images using an Optimized Hexagonal Mulitilayer Perceptron Neural Network

The image capture and storage stages of palm and other biometric image applications is susceptible to various forms of noise due to varying lighting conditions and the type of image-input process employed. In this paper, we present a method for improving palmprint image contrast and detail using hexagon-shaped fully connected feedforward neural network with a single hidden layer, similar to the ones that have been used in literature for prediction and regression applications. It is trained, using Outputweight- optimized Back Propagation (OWO-BP), to remove various forms of noise added to the input images, producing an image where the fine details of the principal lines and wrinkles are highlighted. The training is performed using sub-images of the 32x32 pixel gray-scale images of the central palm area. This method offers a viable, fast-learning and one-stop alternative to the application of spatial and spectral image processing techniques such as unsharp masking, spatial filtering and FFT-based sharpening algorithms. The network can also be applied to image restoration problems, if the noisy input image is viewed as a degraded version of the target, enhanced image.


A Study of speaker identification using phoneme-information


Edge Detection of Eye Region using Genetic Algorithm

In this paper, we present a new edge detection method of eye region from a color video sequence. Our purpose is to develop the practical interface using eye movement. In the proposed method, at first, a eye is tracked by the genetic eye tracking method, then, a edge of the palpebral fissure is detected. Difficulty for this detection is determination of a threshold of segmentation of the palpebral fissure and its surrounding region. We try to determine dynamically this threshold by the proposed method. The experimental results shows that the eye is tracked by the genetic eye tracking, and the proposed method can dynamically perform thresholding to detect the palpebral fissure edge. The processing time is almost real-time.


Fast incremental learning algorithm in pattern recognition

In a field of pattern recognition, researches on feature extraction and dimension reduction are actively done by using methods of the principal component analysis (PCA) and the linear discriminant analysis (LDA), etc. to data with a lot of attributes. On the other hand, there is an algorithm named Simple- PCA to solve such problems. This is an approximation algorithm of PCA. The algorithm that added the learning function to this Simple-PCA has not been presented yet. In this paper, we propose an Incremental Simple-PCA that added an incremental learning function to the Simple-PCA. Moreover, we carry out the computer simulation on personal authentications that use face images by incremental learning to verify the effectiveness of the proposal algorithm.


The Proposal of the GA with Sex-determination of the Hymenoptera and Its Applications


The Music Classification Method by Melody Analysis Using the SVM

Recently, users can have large amounts of music thanks to the development of the computer technology. However, it becomes hard to identify the music based on the music content manually. We think that it is necessary to categorize the music automatically. We propose the music classification method by melody analysis using the SVM in this paper. We aim at the accomplishment of the automatic music classification. Finally, computer simulations are done by classifying real music data in order to show the effectiveness of the proposed method.


Apparent Age Feature Extraction by Empirical Mode Decomposition


Benefits of the Animal Assisted Therapy -- Comparison the EEG features

The animal assisted therapy (AAT) has been known to have psychological and social effects on human being. However, scientific research is insufficient yet. Because, it is difficult to have done in the medical clinic. Furthermore, government can not allow as the medical care because of nothing of clear proof. Therefore, in order to show a scientific basis of the AAT effects, we analyze the electroencephalogram (EEG). Previous study using the stuffed dog has a problem that healing effects from the stuffed dog are little. Therefore, we used a real friendly dog at this experiment to give more healing effects on the subjects. As the results, mental conditions of subjects changed and the brain-wave patterns is confirmed. Finally in this paper, we show the relation between the EEG and mental stability.


Multiple Faces Detection in Real Time using Neural Networks

Proceedings of the 6th International conference on Computational Intelligent, Man Machine Systems and Cybernetics


The Proposal of the EEG Characteristic Extraction Method by Using the FCM

In this paper, we classify the human conditions (before meal, after meal, before smoking, and after smoking). Moreover, we extract the frequency characteristic of conditions. First of all, we measure the EEG data for subjects by using the simple electroencephalograph. Then, the EEG feature is extracted by using the singular value decomposition (SVD). We classify the human conditions by using the fuzzy cmeans (FCM). Moreover, we perform questionnaires for subjects, in order to analyze the data. Then, in order to show the effectiveness of the proposed method, computer simulations are done.


Image Classification Method Using Evolutionary Regional Segmentation

In this paper, we propose the automatic image segmentation method for scene analysis based on color features. The search engines for keyword image retrieval are used on website. However, these search engines usually have some problems. For example, their retrieve images are not suitable image that we don't want to get. Therefore, it is necessary to recognize the images for improving the accuracy of searching. The image segmentation method is considered as helping to recognize the images. The problem of the image segmentation is that the user must decide the thresholds for segmentation. Therefore, the genetic algorithm is used to decide the thresholds for the image segmentation. A supervised image is made based on one of the scene images. Using the color feature values from supervised image, more than 100 test images are segmented into some regions well.


The Sound Emphasis Using the Double Structured ICA

Recently, an independent component analysis (ICA) is paid to attention as a method for classifying an independent signal from assembly of different signals. There are a lot of studies using the ICA, for example, the field of image processing, speech recognition, and electroencephalogram (EEG) analysis. Meanwhile, a present musical notation is made by hearing the sound with a person's ear. However, the musical notation is so difficult, because each subject result of the musical notation is different, and the musical notation has many costs. Therefore, we propose a sound emphasis method using the double structured ICA. Then, we aim to obtain the emphasized sounds, for example, the high pitch sound and the low pitch sound. Furthermore, in order to show the effectiveness of the proposed method, we show the computer simulations by using the real data.


Biological Signal Recognition by Fast Statistical Learning Algorithm

This paper presents an improved statistical learning algorithm, which is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). Generally, FLDA has to carry out minimization of a within-class variance. However the inverse matrix of the within-class covariance matrix could not be obtained. In order to overcome this difficulty, a new iterative feature generation method, the simple-FLDA was proposed by authors. In this paper, a further improvement algorithm is introduced into the simple-FLDA and its effectiveness is demonstrated for biological signal recognition problems.


An Individual Preference Detection System Using the EEG

There are a lot of reports concerning the relation between the EEG and the preference in the analysis of the biosignal. Moreover, attention is currently focused on the aroma, for example, aromatherapy. In this paper, personal preference using olfactory stimulus and the EEG are taken out. We propose the extraction method of the EEG features by the factor analysis. We measure the EEG when smelling favorite odor and dislikeable odor by the simple electroencephalograph. Then, the feature data of the EEG are extracted by the proposed method. Furthermore, in order to show the effectiveness of the proposed method, we demonstrate simulation examples. From the simulation result, it was confirmed that the proposed method works well.


The Extraction of the Coloration Pattern for Personal Room Design

We proposed a new type decision support system of modeling the room designs suited for personal preference, and extract the coloration pattern from personal room designs. The color design is one of the most important elements that influence the deciding the image of products. We always pay attention to the coloration patterns, the preference to colors is differed to individual personality. Nevertheless, modeling the room design is depended on the designer's sensitivity. Therefore, the technique of modeling the various room designs people satisfy is needed. In this paper, we propose automatic modeling system of the room design using interactive genetic algorithm (IGA). The proposed system can help a decision support of modeling the various room designs, and reduce the burdens of the design evolution. We simulated the proposed system against some subjects. In addition, from the designs data of the simulation, we extract the each subject's coloration pattern.


Character Recognition of Malaysian Vehicle License Plate by Neural Networks

This paper presents a method to recognize characters of vehicle license plate in Malaysia by using image processing techniques and a neural network (NN). Vehicle license plate recognition is one of important techniques that can be used for the identification of vehicles all over the world. There are many applications such as entrance admission, security, parking control, airport or harbor cargo control, road traffic control, speed control, toll gate automation and so on. In this paper, techniques for a dynamic image analysis including vehicle detection, license plate segmentation, and character extraction are utilized, which include peculiar knowledge on Malaysian license plate. NN is then used for separated character recognition. The training of NN is carried out using features with boundary coding and gradient components. Computer simulations show that character segmentation and recognition can be effectively carried out using the present method.


Construction of Online Tuning System based on Wrist EMG

Recently, studies of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, the individual variation of EMG is large, and its repeatability is low. Furthermore, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist operations using EMG which was measured from the wrist, the range of application will extend furthermore. In this research, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In the previous research, we proposed a wrist motion recognition system by using Simple-PCA and Simple-FLDA to reduce the number of inputs to a neural network. However, the recognition accuracy was changed considerably by the individual. Therefore, it is thought that the construction of an online tuning system that can consider the individual variation is necessary. In this paper, we propose an improvement algorithm of Simple-PCA named Incremental Simple-PCA that updates eigenvectors sequentially by adding a new data. Moreover, we try the construction of the online tuning system by using this algorithm.


A Visualization of Genetic Algorithm Using the Pseudo-color

In this paper, we propose the method for determining the schema interactively based on a visualization method to grasp the search process and results in the binary-coded genetic algorithm. We focus on the chromosome structure, the fitness function, the objective function, and the association among these parameters, because these parameters are decided interactively and very difficult to disentangle their effects. We can indicate the most important parameters optimized in visually. The visualization method is indicated all individuals of the current generation using the pseudo-color. Moreover, the visualization method proposed uses to support for determining the schema. In order to show the effectiveness of the proposed method, we apply the proposed method to the zero-one knapsack problems.


The Room Design System of Individual Preference with IGA

In this paper, we illustrate a decision support system of modeling the room designs suited for personal preference. The preference to colors is differed to individual personality. Nevertheless, modeling the room design is depended on the designer's sensitivity until now. Therefore, the technique of modeling the various room designs people satisfy is needed. In this paper, we propose automatic modeling system of the room design using interactive genetic algorithm (IGA). IGA has the advantage of learning the user's interactive evaluation, which is applied to the production of new room designs suited for the personal preference. The proposed system can help a decision support of modeling the various room designs, and reduce the burdens of designers significantly. In proposed system, the design evolution of IGA can reflect the user preference, and we present the effectiveness of the system by simulations.


An Apparent Age Estimation System Using the Evolutionary Algorithm

The age is one of important information in our living. If the age estimation that uses face image by computer becomes possible, it is thought that the age estimation assumes an important role in various scenes. In this paper, we propose an age estimation by using the supervised SOM. Further, the important features for the age estimation are selected by the GA.


Detecting Method of Music to Match the User's Mood in Prefrontal Cortex EEG Activity Using the GA

In this paper, we propose a method for detecting the mood much music for prefrontal cortex electroencephalogram (EEG) activity. The analyzed EEG frequencies contain significant and immaterial information components. We focused on the combinations of the significant frequency. These frequency combinations are thought to express personal features of EEG activity. In the proposed method, we calculate the spectrum of these frequency combinations rates that does not include the noise frequency components and evaluates whether the music matches the user's mood through a simple threshold processing. Then, a genetic algorithm (GA) is used to specify the frequency of personal features on the EEG. The threshold vale used the threshold processing is the average value of the spectrum rates specified EEG frequency combinations. Finally, the performance of the proposed method is evaluated using real EEG data.


Detection of the Human-Activity Using the FCM

In this paper, we propose the detection system of the human activity by using the electroencephalograms (EEG). First, we measure the EEG data for subjects. In most of all conventional studies, the EEG having a lot of sensors is used. Therefore, subjects must eat or smoke while using the EEG interface. However, this situation is not practical for subjects. In this study, taking account of the burden of subjects, we use only one measurement point 'FPI'. First, we measure the EEG data and the EMG data for subjects. Then, the EEG feature is extracted by using the singular value decomposition (SVD). From the result, we classify the EEG pattern by the fuzzy c-means (FCM). If we cannot classify the EEG pattern into each activity, the discriminant analysis (DA) is used. We consider the EEG features of activities. Then, in order to show the effectiveness of the proposed method, computer simulations are done.


Automatic Decision Method of Parameters in the Maximum Distance Algorithm

The maximum distance algorithm has been considered to be effective for an image segmentation of color scenery images. However, in the maximum distance algorithm, the parameter which decides the end term of the clustering is set in advance. The applicable value of this parameter depends on the individual image. Therefore, we propose the automated adjustment method of the maximum distance algorithm's parameter for the image segmentation of scenery images. First, "image density" is defined as the measure to evaluate the complexity of each image. Image density is calculated by difference between average of color value and color value of each pixel. Then, the relation of the image density and applicable value of the maximum distance algorithm is investigated. This investigation enables us the automated adjustment method of maximum distance algorithm's parameter fitting the image density of individual image. In this paper, the computer simulation is done for the purpose of comparing the conventional method and proposed method. There is the regulation between appropriate parameter in maximum distance algorithm. The experiment with about 100 images shows the effectiveness of the proposed method.


Method for Detecting Music to Match the User's Mood in Prefrontal Cortex Electroencephalogram Activity Based on Individual Characteristics

Proc. of 2007 IEEE International Conference System, Man and Cybernetics

In this article, we propose a method for detecting music to match a users mood in prefrontal cortex electroencephalogram (EEG) activity. The frequencies of the EEG analyzed are the components that contain significant and immaterial information. We focused on the significant frequency combinations. These frequency combinations are thought to express individual characteristics of EEG activity. The proposed method calculates the percentage of the spectrum of these frequency combinations that does not include the noise frequency components and evaluates whether the music matches the users mood through a simple threshold processing. A genetic algorithm (GA) is used to specify the frequency of individual characteristics on the EEG. A threshold value that used the threshold processing is determined in the GA. Finally, the performance of the proposed method is evaluated using real EEG data.}


The EEG Feature Extraction Method of Listening to Music Using the Genetic Algorithms and Latency Structure Model

It is known that an electroencephalogram (EEG) is characterized by the unique and personal features of an individual. The EEG frequency components are contained the significant and immaterial information, and then each importance of these frequency components is different. These combinations are often unique like individual human beings and yet they have underlying basic characteristics. We think that these combinations and/or the importance of the frequency components show the personal features. Therefore we propose the two techniques for estimating the personal features. A simple genetic algorithm is used for specifying these frequency combinations. Other technique, a real-coded genetic algorithm is used for estimating the importance of EEG frequency components. Then a latency structure model based on the personal features is used for extracted the feature vector of the EEG. Furthermore, the visualization map is used for evaluating the extracted feature vector of the EEG. In order to show the effectiveness of the proposed methods, the performance of the proposed method is evaluated using real EEG data.


Incremental learning method in wrist EMG recognition system

In a field of pattern recognition, researches of feature extraction and dimension reduction using the Simple-PCA that is an approximation algorithm of the principal component analysis (PCA) are actively conducted. In such a statistical method, a lot of algorithms that perform incremental learning by using new incremental data exist. For example, there is an algorithm named Incremental PCA for PCA. However, the algorithm capable of performing incremental learning to the Simple-PCA has not been presented yet. In this paper, we propose Incremental Simple-PCA that introduces an incremental learning function to the Simple-PCA. Moreover, the effectiveness of this algorithm is verified in EMG pattern recognition.


Appearance-age feature extraction from facial image based on age perception

An age is relatively important information in the various features which are shown human from face. It is possible to apply it to an amusement and an aesthetic surgery by analyzing the appearance-age feature which is used for age perception in a facial region. In this paper, we analyze facial feature of appearance-age based on age perception. In order to construct the appearance-age database, age estimation experiment is performed to the HOIP facial database by 15 subjects. The appearance-age feature used potentially when human performs age estimation is decided by using the genetic algorithms and the neural network. By using the proposed method, facial features that influenced on the appearance-age in each gender was confirmed. Moreover, it is suggested that that appearance-age feature which is used for age estimation is different in each generation when human performs age estimation.


A Proposal of Adaptive Graininess Suppression Method

Previous studies of image restoration for noise image were based on mask processing. These conventional noise removal methods represented from mask processing have issue of definition degradation to accompany spacial processing. In this paper, we propose a graininess suppression method based on edge shape. In this method, we detect edges from a noise image and perform graininess suppression for this image based on edge information. On the edge detection, we execute an image transformation for an image that enables us to extract edge by making principal component image. Moreover, we use the canny edge detection operator that can detect a weak edge that relates to a real edge, and do not detect a lie edge. In the suppression process, we use Wiener filter that can restore an noise image without making a complete edge map and the original signal map. We have that the present method for the noise added images to verify effectiveness and have confirmed this.


A Design of the EEG feature detection and condition classification

In this paper, we classify the human conditions (before and after meal, before and after smoking) and extract the frequency feature of conditions by using the electroencephalograms (EEG). First, we measure the EEG data. Then, we classify the conditions by using the principal component analysis (PCA). Moreover, the EEG data is reconstructed by using the questionnaires and the result of classification. From the result, we consider ideal circumstance for the EEG measurement. Finally, the EEG data is decompressed to consider the EEG features of conditions. Then, in order to show the effectiveness of the proposed method, computer simulations are done.


Apparent age estimation system based on age perception

In this paper, we propose the new age estimation system with the GA and the LVQ. The GA is used in the feature extraction of faces at each age and the faces are estimated by using the LVQ in this paper. Moreover, features are analyzed in the age by using the selected feature data. Furthermore, in order to show the effectiveness of the proposed method, we show the simulation examples. From the simulation results, we can confirmed that the proposed method works well.


A Design of the Preference Acquisition Detection System

Recently in the world, there are a lot of reports concerning the relation between the EEG and the preference in the analysis of the biosignal. In this paper, personal preference using olfactory stimulus and the EEG are taken out. We measure the EEG when smelling favorite odor and dislikeable odor. Then, the feature data of the EEG are extracted by the proposed method. Furthermore, in order to show the effectiveness, we demonstrate simulation examples. From the simulation result, it was confirmed that the proposed method works well.


Fast Statistical Learning Algorithm for Feature Generatio

This paper presents an improved statistical learning algorithm for feature generation in pattern recognition and signal processing. It is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). Recently FLDA has been often used in many fields, especially face image recognition. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome the difficulty of FLDA, a new iterative feature generation method, the simple-FLDA was proposed by authors. In this paper, further improvement is introduced into the simple-FLDA and its effectiveness is demonstrated for preliminary personal identification problem.


Automatic Utterance Segmentation Tool for Speech Corpus

We collect the speech data for investigating an intra-speakers' speech variability over a short and long time. In general, to reduce the load of speakers, the speech data are collected as one file from collecting start to collecting end. Hence, there are some noises, non-speech sections and mistaken sections in this file. Consequently, we must segment this file into individual utterances and select the useful utterances. This process requires a lot of time and efforts. In this paper, we propose an automatic utterance segmentation tool for dividing the collected speech data. The proposed tool is composed of four processes, which are a voice activity detection, speech recognition, a DP matching, and a correct of speech section. For evaluating the proposed tool, we conduct the evaluation experiments using a female speaker's speech data in our corpus. Experimental results show that the proposed method can reduce a filing time by 90% compared to a manual filing. In This paper, first, we introduced the large speech corpus. This speech corpus contains is the speech data collected by specific speaker over long and short time periods. And, we explained the automatic utterance segmentation tool which we made in the case of corpus build. And inspected the validity. As a result, it was demonstrated that the automatic utterance segmentation tool was high-performance. Furthermore, it was demonstrated that speech corpus build became simple by using the automatic utterance segmentation tool.


Analysis of Variation on Intra-Speakers Speech Recognition Performances

Even if a speaker uses a speaker-dependent speech recognition system, speech recognition performance varies. However, the relationships between intra-speaker's speech variability and speech recognition performance are not clear. To investigate these relationships, we have been collecting speech data since November 2002. In this paper, we analyze the relationships between intra-speaker's speech variability and the phoneme accuracy by a correlation analysis. Analyzed results showed the strong negative correlation between the phoneme accuracy and the speaking rate. The correlation coefficient indicated -0.77. Moreover, we can see that the phoneme accuracy is correlated with the temperature in the recording room and the humidity difference.


Proc. of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'07)


Practical Genetic Eye Detection System: Which Is Better, Optical or Digital Zoom?

Proc. of 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing

In this paper, the effectiveness of the digital zooming for the size and orientation invariant eye tracking system in an active scene using template matching and genetic algorithms is described. Consideration the practical use, it is better that the equipment is simple and low cost. In our proposed system, we use a ``web camera'', which requires no specific interface. This type of camera is easily obtainable, however does not have optical zooming function. Therefore, we try to use the digital zooming instead of the optical zooming. However, the quality of image is a problem. In this paper, the effectiveness of the digital zooming for the eye detection is demonstrated. Our proposed system is based on template matching using genetic algorithms (GAs). Only one artificial template is used for every human user. Various geometric changes of iris can be supported by genetic algorithms. As a result of comparative experiments, the eye detection accuracy of the digital zooming is little better than the optical digital zooming, though the target image is coarse by the digital zooming


Recently, independent component analysis (ICA) is paid to attention as a method for classifying an independent signal from assembly of different signals, and there are a lot of researches using the ICA. Then, in this paper, we pay attention to the field of voice recognition, and we aim at the achievement of the part classification system of music using the ICA. Furthermore, in order to show the effectiveness of the proposed method, we show the simulation examples.


An Age Estimation System Based on the LVQ


Discrimination of Wrist EMG Signals using a Statistical Method

In this study, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In the previous study, the recognition accuracy was changed considerably by the individual. Therefore, we thought that the construction of an online tuning system that can consider the individual variation was necessary. In this paper, we propose an improvement algorithm of Simple-PCA that changes eigenvectors sequentially by adding data. Moreover, the construction of the online tuning system is carried out by using this algorithm.


Preference Acquisition by Using Olfactory Stimulus and EEG

In this paper, personal preference using olfactory stimulus and the EEG are taken out. We propose the extraction method of the EEG features by the factor analysis. Then, we classify the odor into favorite and dislikeable. Furthermore, in order to show the effectiveness of the proposed method, we demonstrate simulation examples. From the simulation result, it was confirmed that the proposed method works well.


In this paper, we classify the human conditions (before meal, after meal, before smoking, and after smoking) by using the electroencephalograms (EEG).We propose the electroencephalograms (EEG) characteristic extraction method by using the principal component analysis (PCA). Then, in order to show the effectiveness of the proposed method, computer simulations are done by classifying the EEG pattern in the proposed method.


A Design of the Mapping Method of Color and Kansei

We have various sensibilities to various colors, and the colors have a great influence on deciding the image of products. We have always paid attention to the colors, the image to colors depends on individual personality. Therefore, the technique of analyzing image to colors is needed in KANSEI (Japanese term) information processing field. In this paper, we investigate the evaluation between colors and images against subjects, and analyze the relationship to individual. We propose the method of the KANSEI extraction from each subject data by using 2 grade-type sand grass neural network. Finally, we describe the KANSEI extraction information visually. Furthermore, in order to show the effectiveness of the proposed method, we show the simulation examples. From the simulation results, it was confirmed that the proposed method works well.


Study of Relationships Between Intra-Speaker's Speech Variability and Speech Recognition Performance

2006 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2006)

Even if a speaker uses a speaker-dependent speech recognition system, speech recognition performance varies. For this reason, speech quality is varied by some factors, including emotion, background noise, and so on, even though the speaker and utterance remain constant. However, the relationships between intra-speaker's speech variability and speech recognition performance are not clear. Hence, we focus on the intra-speaker's speech variability which affects the speech recognition performances. To investigate these relationships, we have been collecting speech data since November 2002. Using a part of the speech corpus, we conducted speech recognition experiments. In this paper, we analyze the relationships between intra-speaker's speech variability and the phoneme accuracy by using the correlation analysis. For factors of the correlation analysis, we use a number of errors, a speaking rate, a likelihood. Analysis results show a strong correlation between the number of the substitution errors and the phoneme accuracy although the correlations of the number of the deletion and the insertion errors are low. Therefore, it is considered that there are overlaps between phonemes since the feature parameters vary at each speaking rate. For improving the phoneme accuracy, it is needed that we study a method which discriminates phonemes. On the other hand, although the correlation between the phoneme accuracy and the speaking rate seems to be low, a strong correlation between the speaking rate and the number of deletion errors and insertion errors are found. Since the number of the insertion errors and the number of the deletion errors were in the counterbalance relation, the correlation between the speaking rate and the phoneme accuracy was low. However, we consider that it is needed to normalize the speaking rate because the speaking rate influences on the number of the deletion and the insertion errors.


Extraction of the Liver Tumor in CT Images by Real-coded Genetic Algorithm

The purpose of this work is the construction of an automatic diagnosis support system for CT images in order to reduce the doctor's load. Toward this end, in this paper, a method to extract liver tumors in CT images using a real-coded genetic algorithm is proposed. Conventionally, a threshold is necessary to extract an object from an image. However, such a method is not effective for CT images because gray scale values are different in each image. Therefore, in this paper, we propose the method for extracting the tumor in the liver from the CT image without the need of a threshold. In this method, a polygon enclosure of the liver tumor is extracted using a GA.


Isolated Syllable Recognition on Mobile Terminal Devices

Proc. of IASTED International Conference on Computational Intelligence

The International Association of Science and Technology for Development

The aim of this work is the establishment of isolated syllable recognition for Japanese language on mobile terminal devices and/or the retry after the failure in speech recognition. The subjects in this study are five Japanese vowels. The experiment was carried out on 100 sets of five Japanese vowels from four men. The experiment for recognition was carried out using the Akamatsu transform. The experiment demonstrated the effectiveness of the proposed technique for recognizing the isolated 100 sets. This paper serves as an introduction on the use of the Akamatsu transform in speech recognition and does not, as yet, compare the results with existing methods.


Pattern Recognition of EMG Signals by the Evolutionary Algorithms


Proposal for the Extraction Method of Personal Comfort and Preference by the EEG Maps


Designing of the Color KANSEI Information Map Considering the Individual Characteristic by Using Multivariable Analysis

The color design is one of the most important elements that influence the impression of products, therefore the technology which understands and reflects the consumer's sensibility is needed in the color design. Especially, the color coordination system that connects colors with impressions is expected as the system supporting the color design. Therefore, in the field of the KANSEI engineering, researches that model the correlation of colors (the arrangement of the color) and impression words have been done. However, the impression received from the color is various by individual, therefore, it is necessary to model the KANSEI information to each individual. In this paper, we analyze the data of the evaluation questionnaire of the color for the subject, and propose the method of extracting the KANSEI information of the subject. Concretely, by using the multivariable analysis, the correlation with an amount of the sensibility and the color feature is found. As a result, it is thought that the tendency of impressions to colors can be expressed in the sight.


Pattern Recongnition of EMG signals by the Evolutionary Algorithms

In this paper, we propose a method of function derivation for performing recognition of wrist operations by the electromyographic (EMG) signals extracted from 4-channel EMG sensor. In designing a recognition device of operations, the important fewer amount of information is needed for reduction of cost and accuracy improvement in practical systems. Then, date mining is performed by specifying important frequency bands using genetic algorithm (GA) and neural network (NN). The derivation of function for generating a feature vector is performed only using the important frequency bands obtained by GA and NN. In this case, the feature vector which consists of frequency spectrum to be used is mapped to another space. We use the generated function as an input feature to perform recognition experiments of EMG signal by NN. Finally, the effectiveness of this method is demonstrated by means of computer simulations.


A Design of an Age Estimation System Using the SOM

In this paper, we propose the new age estimation system using the GA and SOM. The GA is used in the feature extraction of faces at each age. Especially, we use the interactive GA. The faces are classified by using the SOM. Moreover, features are analyzed in the age by the selected parts of faces. Furthermore, in order to show the effectiveness of the proposed method, we show the simulation examples.


Fast Face Extraction from Near-Infrared Camera Images

This paper proposes and evaluates the use of digital image processing for profiling a key groove. The first step of research is taking a cross-sectional image of the key. Exploiting Laplacian edge enhancement to acquire a contour of the key grooves. Transform the image data to surface data presented by coordinate (x,y). This information is used as an input for CNC coding program. The duplication of key blank is performed by CNC. The tolerance examination is conducted by comparing a calculated image proposed to the key-groove with a measured value from vernier. The error is less than 5%


Recognition of Wrist Motion Pattern by EMG

Recently, studies of artificial arms and pointing devices using ElectroMyoGram (EMG) have been actively done. However, the individual variation of EMG is large, and its repeatability is low. Furthermore, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist operations using EMG which was measured from the wrist, the range of application can extend furthermore. In this study, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In particular, as an early stage, we propose a wrist motion recognition system. First, we execute the Fourier transform to the signal for feature extraction. Next, we experiment it by using neural networks after the dimensional reduction by using simple-PCA and simple-FLDA to reduce the number of inputs to NN. It was confirmed that the present approach was one of the techniques which were effective in the wrist recognition experiment.


The Proposal of the EEG Characteristics Extraction Method in Weighted Principal Frequency Components Using the RGA

An EEG has frequency components which can describe most of the significant features. These combinations are often unique like individual human beings and yet they have underlying basic features. These frequency components are contained the important and/or not so important components, and then each importance of these frequency components are different. The real-coded genetic algorithm (: RGA) is used for selecting and being weighted the principal characteristic frequency components. We attempt to construct mental change appearance model (: MCAM) of only one measurement point. In order to show the effectiveness of the proposed method, computer simulations are carried out by using real data.


A Simple 3D Edge Template for Pose Invariant Face Detection

Proc. of Knowledge-Based & Intelligent Information & Engineering Systems (KES)


Automatic Extraction of Heart Contour using SNAKES

Recently, as sophisticated medical instruments have been developed, the internal state of a human body has become well-known. In particular, in the examination of heart, non-surgical and non-intrusion ultrasonic diagnostics are generally utilized. However, the diagnosis depends on the doctor's subjectivity and experience. Furthermore, the doctor's burden increases because the contour of the heart is extracted by manual operations. Therefore, the development of a system that offers objective information in addition to a subjective judgment of the doctor by extracting and analyzing quantitative information from image analysis by computers is necessary. In this paper, the contour of the chamber wall is extracted by using an active contour model SNAKES as a preprocessing of the heart disease detection. The contour has been extracted by using SNAKES of the expansion model from the inner surface of heart


Feature Extraction in Listening to the Music Using Statistical Analysis of the EEG

In order to solve stress problems, researchers have studied healing, especially the music therapy. It is mentioned that objective evaluation of the music therapy is an important assignment, and some researchers have tried objective measurement based on physiological change. In this paper, the purpose is extraction of features that may be influenced by the music. We pay attention to EEG (electroencephalogram) as an objective and absolute scale. This paper proposes a method that extracts features of the EEG by the CDA (canonical discriminant analysis). From the result of the experiment, it is suggested that the CDA extracts the features influenced by the individual and the music type