IEICE TRANSACTIONS on Information
A New Iris Recognition Method Using Independent Component Analysis
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.11 pp.2573-2581
- Publication Date
- 2005/11/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.11.2573
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Seung-In NOH, Kwanghyuk BAE, Kang Ryoung PARK, Jaihie KIM, "A New Iris Recognition Method Using Independent Component Analysis" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 11, pp. 2573-2581, November 2005, doi: 10.1093/ietisy/e88-d.11.2573.
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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.11.2573/_p
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@ARTICLE{e88-d_11_2573,
author={Seung-In NOH, Kwanghyuk BAE, Kang Ryoung PARK, Jaihie KIM, },
journal={IEICE TRANSACTIONS on Information},
title={A New Iris Recognition Method Using Independent Component Analysis},
year={2005},
volume={E88-D},
number={11},
pages={2573-2581},
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.},
keywords={},
doi={10.1093/ietisy/e88-d.11.2573},
ISSN={},
month={November},}
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TY - JOUR
TI - A New Iris Recognition Method Using Independent Component Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 2573
EP - 2581
AU - Seung-In NOH
AU - Kwanghyuk BAE
AU - Kang Ryoung PARK
AU - Jaihie KIM
PY - 2005
DO - 10.1093/ietisy/e88-d.11.2573
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2005
AB - 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.
ER -
English
