Hyoun-Joo Go

Iris pattern recognition using fuzzy LDA method

This paper proposes an iris pattern recognition algorithm as one of biometric techniques applied to identify a person using his/her physiological characteristics. Since the iris pattern of human eye has an unique and invariant texture, we can use it as a biometric key. First, we obtain the feature vector from the fuzzy LDA after performing 2D Gabor wavelet transform. And then, we compute the similarity measure based on the correlation. Here, since we use four matching values obtained from four different directional Gabor wavelets and select the maximum value among them, it is possible to reduce the recognition error. To show the usefulness of the proposed algorithm, we applied it to an iris database consisting of 300 iris patterns extracted from 50 subjects and finally got more higher than 90% recognition rate.

Face Recognition for Expressive Face Images

In this paper, we deal with a face recognition method for the expressive face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated. The face recognition becomes a very difficult problem if we consider the facial expression. This situation requires a robust face recognition algorithm. So, we use a fuzzy linear discriminant (LDA) algorithm with the wavelet transform. The fuzzy LDA is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information.

Facial Expression Recognition using ICA-Factorial Representation Method

In this paper, we proposes a method for recognizing the facial expressions using ICA(Independent Component Analysis)-factorial representation method. Facial expression recognition consists of two stages. First, a method of Feature extraction transforms the high dimensional face space into a low dimensional feature space using PCA(Principal Component Analysis). And then, the feature vectors are extracted by using ICA-factorial representation method. The second recognition stage is performed by using the Euclidean distance measure based KNN(K-Nearest Neighbor) algorithm. We constructed the facial expression database for six basic expressions(happiness, sadness, angry, surprise, fear, dislike) and obtained a better performance than previous works.

A Multimodal Emotion Recognition Using the Facial Image and Speech Signal

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facial expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.

Emotion Recognition Method from Speech Signal Using the Wavelet Transform

In this paper, an emotion recognition method using speech signal is presented. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. The proposed recognizer have each codebook constructed by using the wavelet transform for the emotional state. Here, we first verify the emotional state at each filterbank and then the final recognition is obtained from a multi-decision method scheme. The database consists of 360 emotional utterances from twenty person who talk a sentence three times for six emotional states. The proposed method showed more 5% improvement of the recognition rate than previous works.

Development of Advanced Personal Identification System Using Iris Image and Speech Signal

This proposes a new algorithm for advanced personal identification system using iris pattern and speech signal. Since the proposed algorithm adopts a fusion scheme to take advantage of iris recognition and speaker identification, it shows robustness for noisy environments. For evaluating the performance of the proposed scheme, we compare it with the iris pattern recognition and speaker identification respectively. In the experiments, the proposed method showed more 56.7% improvements than the iris recognition method and more 10% improvements than the speaker identification method for high quality security level. Also, in noisy environments, the proposed method showed more 30% improvements than the iris recognition method and more 60% improvements than the speaker identification method for high quality security level.

Steganography based Multi-modal Biometrics System

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

Face Recognition Under Ubiquitous Environments

This paper propose a facial recognition method based on an ubiquitous computing that is one of next generation intelligence technology fields. The facial images are acquired by a mobile device so-called cellular phone camera. We consider a mobile security using facial feature extraction and recognition process. Facial recognition is performed by the PCA and fuzzy LDA algorithm. Applying the discrete wavelet based on multi-resolution analysis, we compress the image data for mobile system environment. Euclidean metric is applied to measure the similarity among acquired features and then obtain the recognition rate. Finally we use the mobile equipment to show the efficiency of method. From various experiments, we find that our proposed method shows better results, even though the resolution of mobile camera is lower than conventional camera.

Iris Recognition Using the 2-D Gabor Filter

This paper deals with the iris recognition as one of biometric techniques which are applied to identify a person using his/her behavior or congenital characteristics. The iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D iris pattern having a property of size invariant and divide it into 24 sectors which are further through three types of 2D Gabor filters. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use three different matching values obtained from three different directional Gabor filters and select the maximum value among them, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 50 iris patterns extracted from 10 subjects and finally get more higher than 90% recognition rate.