An Analysis of IrisCode

被引:38
作者
Kong, Adams W. K. [1 ]
Zhang, David [2 ]
Kamel, Mohamed S. [3 ]
机构
[1] Nanyang Technol Univ, Forens & Secur Lab, Sch Comp Engn, Singapore 639798, Singapore
[2] Hong Kong Polytech Univ, Biometr Res Ctr, Dept Comp, Kowloon, Hong Kong, Peoples R China
[3] Univ Waterloo, Pattern Anal & Machine Intelligence Res Grp, Waterloo, ON N2L 3G1, Canada
关键词
Biometrics; Daugman algorithm; Gabor filter; iris recognition; palmprint recognition; phase; RECOGNITION;
D O I
10.1109/TIP.2009.2033427
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
IrisCode is an iris recognition algorithm developed in 1993 and continuously improved by Daugman. It has been extensively applied in commercial iris recognition systems. IrisCode representing an iris based on coarse phase has a number of properties including rapid matching, binomial impostor distribution and a predictable false acceptance rate. Because of its successful applications and these properties, many similar coding methods have been developed for iris and palmprint identification. However, we lack a detailed analysis of IrisCode. The aim of this paper is to provide such an analysis as a way of better understanding IrisCode, extending the coarse phase representation to a precise phase representation, and uncovering the relationship between IrisCode and other coding methods. Our analysis demonstrates that IrisCode is a clustering algorithm with four prototypes; the locus of a Gabor function is a 2-D ellipse with respect to a phase parameter and can be approximated by a circle in many cases; Gabor function can be considered as a phase-steerable filter and the bitwise hamming distance can be regarded as a bitwise phase distance. We also discuss the theoretical foundation of the impostor binomial distribution. We use this analysis to develop a precise phase representation which can enhance accuracy. Finally, we relate IrisCode and other coding methods.
引用
收藏
页码:522 / 532
页数:11
相关论文
共 33 条
[1]  
[Anonymous], COMMUNICATION
[2]  
Bae K, 2003, LECT NOTES COMPUT SC, V2688, P838
[3]   Iris individuality: A partial iris model [J].
Bolle, RM ;
Pankanti, S ;
Connell, JH ;
Ratha, NK .
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, 2004, :927-930
[4]   How iris recognition works [J].
Daugman, J .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2004, 14 (01) :21-30
[5]   HIGH CONFIDENCE VISUAL RECOGNITION OF PERSONS BY A TEST OF STATISTICAL INDEPENDENCE [J].
DAUGMAN, JG .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1993, 15 (11) :1148-1161
[6]   New methods in iris recognition [J].
Daugman, John .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2007, 37 (05) :1167-1175
[7]   Probing the uniqueness and randomness of IrisCodes: Results from 200 billion iris pair comparisons [J].
Daugman, John .
PROCEEDINGS OF THE IEEE, 2006, 94 (11) :1927-1935
[8]  
Ea T., 2005, 2005 48th IEEE International Midwest Symposium on Circuits and Systems (IEEE Cat. No. 05CH37691), P1207
[9]   THE DESIGN AND USE OF STEERABLE FILTERS [J].
FREEMAN, WT ;
ADELSON, EH .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1991, 13 (09) :891-906
[10]   A fast search algorithm for a large fuzzy database [J].
Hao, Feng ;
Daugman, John ;
Zielinski, Piotr .
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2008, 3 (02) :203-212