A New Iris Recognition Method Using Independent Component Analysis

Authors:
Seung-In Noh;Kwanghyuk Bae;Kang Ryoung Park;Jaihie Kim
Affiliations:
The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. E-mail: sinoh@yonsei.ac.kr,;The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. E-mail: sinoh@yonsei.ac.kr,;The author is with the Division of Media Technology, Sang Myung University, Seoul, Korea.;The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. E-mail: sinoh@yonsei.ac.kr,
Venue:
IEICE - Transactions on Information and Systems
Year:
2005

Citing 0
Cited 4

Iris recognition based on score level fusion by using SVM

Pattern Recognition Letters
Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Iris recognition in mobile phone based on adaptive gabor filter

ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Iris recognition based on bidimensional empirical mode decomposition and fractal dimension

Information Sciences: an International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

In a conventional method based on quadrature 2D Gabor wavelets to extract iris features, the iris recognition is performed by a 256-byte iris code, which is computed by applying the Gabor wavelets to a given area of the iris. However, there is a code redundancy because the iris code is generated by basis functions without considering the characteristics of the iris texture. Therefore, the size of the iris code is increased unnecessarily. In this paper we propose a new feature extraction algorithm based on independent component analysis (ICA) for a compact iris code. We implemented the ICA to generate optimal basis functions which could represent iris signals efficiently. In practice the coefficients of the ICA expansions are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing individual's iris patterns. Additionally, we introduce a method to refine the ICA basis functions for improving the recognition performance. Experimental results show that our proposed method has a similar equal error rate as a conventional method based on the Gabor wavelets, and the iris code size of our proposed methods is five times smaller than that of the Gabor wavelets.