Byungjun Son

Biometric authentication system using reduced joint feature vector of iris and face

In this paper, we present the biometric authentication system based on the fusion of two user-friendly biometric modalities: Iris and Face. Using one biometric feature can lead to good results, but there is no reliable way to verify the classification. In order to reach robust identification and verification we are combining two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. This system can operate in two modes: to identify a particular person or to verify a persons claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

Discriminant iris feature and support vector machines for iris recognition

In an iris recognition system, the size of the feature set is normally large. As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient iris recognition. In this paper. we present one of the major discriminative learning methods, namely, Direct Linear Discriminant Analysis (DLDA). Also, we apply the multiresolution wavelet transform to extract the unique feature from the acquired iris image and to decrease the complexity of computation when using DLDA. The Support Vector Machines (SVM) approach for comparing the similarity between the similar and different irises can be assessed to have the features discriminative power. In the experiments, we have showed that that the proposed method for human iris gave a efficient way of representing iris patterns.

Iris Recognition System Using Wavelet Packet and Support Vector Machines

In this paper, iris recognition system using wavelet packet and support vector machines is presented. It specifically uses the multiresolution decomposition of 2-D discrete wavelet packet transform for extracting the unique features from the acquired iris image. This method of feature extraction is well suited to describe the shape of the iris while allowing the algorithm to be translation and rotation invariant. The SVM approach for comparing the similarity between the similar and different irises can be assessed to have the feature’s discriminative power. We have showed that the proposed method for human iris recognition gave a way of representing iris patterns in an efficient manner and thus had advantages of saving both time and space. Thanks to the efficiency of the proposed method, it can be easily applied to the real problems.

The Fusion of Two User-friendly Biometric Modalities: Iris and Face

In this paper, we present the biometric authentication system based on the fusion of two user-friendly biometric modalities: Iris and Face. Using one biometric feature can lead to good results, but there is no reliable way to verify the classification. To achieve robust identification and verification we are combining two different biometric features. We specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from the iris and face. And then to obtain Reduced Joint Feature Vector (RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. This system can operate in two modes: to identify a particular person or to verify a persons claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

Multifocus image sequences for iris recognition

We report on an iris recognition system using image sequences instead of single still images for recognition. Image sequences captured at different focus levels provides more information than single still images. Most of the current state-of-the-art iris recognition systems use single still images which are highly focused. These systems does not recognize defocused iris images. The experimental results show that defocused iris images can be correctly recognized if we use multifocus image sequences as gallery images for recognition.

Identification of Humans Using Robust Biometric Features

The size of the feature set is normally large in a recognition system using biometric data, such as Iris, face, fingerprints etc. As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient biometric identification. In this paper, we present one of the major discriminative learning methods, namely, Direct Linear Discriminant Analysis (DLDA). Also, we specifically apply the multiresolution decomposition of 2-D discrete wavelet transform to extract the robust feature set of low dimensionality from the acquired biometric data and to decrease the complexity of computation when using DLDA. This method of features extraction is well suited to describe the shape of the biometric data while allowing the algorithm to be translation and rotation invariant. The Support Vector Machines (SVM) approach for comparing the similarity between the similar and different biometric data can be assessed to have the feature’s discriminative power. In the experiments, we have showed that that the proposed method for human iris and face gave a efficient way of representing iris and face patterns.

Multimodal System by Data Fusion and Synergetic Neural Network

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a persons claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

Fusion of Dimension Reduction Methods and Application to Face Recognition

As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient face recognition. In this paper, we suggest the fusion of Discrete Wavelet Transform(DWT) and Direct Linear Discriminant Analysis (DLDA) for the efficient dimension reduction. The Support Vector Machines (SVM) and nearest mean classifier (NM) approaches are applied to compare the similarity between the similar and different face data. In the experiments, we show that the proposed method is an efficient way of representing face patterns as well as reducing dimension of multidimensional feature.