Efficient Iris Recognition through Improvement of Feature Vector and Classifier
In this paper, we propose an efficient method for personal identification by analyzing iris patterns that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform, and two straightforward but efficient mechanisms for a competitive learning method such as a weight vector initialization and the winner selection. With all of these novel mechanisms, the experimental results showed that the proposed system could be used for personal identification in an efficient and effective manner.
To control the access to secure areas or materials, a reliable personal identification infrastructure is required. Conventional methods of recognizing the identity of a person by using passwords or cards are not altogether reliable, because they can be forgotten or stolen. Biometric technology, which is based on physical and behavioral features of human body such as face, fingerprints, hand shape, eyes, signature and voice, has now been considered as an alternative to existing systems in a great deal of application domains. Such application domains include entrance management for specified areas, and airport security checking system. Among various physical characteristics, iris patterns have attracted a lot of attention for the last few decades in biometric technology because they have stable and distinctive features for personal identification. That is because every iris has fine and unique patterns and does not change over time since two or three years after the birth, so it might be called as a kind of optical fingerprint [1], [2]. Figure 1 shows an image of human iris pattern. Most works on personal identification and verification using iris patterns have been done in the 1990s [4]-[8]. Through these works, we could achieve a great deal of progress in iris-based identification systems much more than we expected. Some work, however, has limited capabilities in recognizing the identity of person accurately and efficiently, so there is much room for improvement of some technologies affecting performance in a practical viewpoint. The main difficulty of human iris recognition is that it is hard to find apparent feature points in the image and to keep their representability high in an efficient way. In addition, the identification or verification process suitable for iris patterns is required to get high accuracy
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