Enhancing iris recognition system performance

Biometrics has become more and more important in security applications. In comparison with many other biometrie features, iris recognition has very high recognition accuracy. Successful iris recognition matching depends on how similar the stored template in database is compared with the introduced template. The main objective of this paper is to introduce a high performance scheme for iris recognition system in which a set of iris images of a given eye are fused to generate a final template. This will not only reduce the storage capacity requirements but also enhance the system's speed during the matching process. The performance of both the proposed algorithm and the overall system will be assessed

[1]  H. J. Wyatt,et al.  A ‘minimum-wear-and-tear’ meshwork for the iris , 2000, Vision Research.

[2]  Miroslav Kubat,et al.  Voting Nearest-Neighbor Subclassifiers , 2000, International Conference on Machine Learning.

[3]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[4]  Zhongliang Jing,et al.  Multi-focus image fusion using pulse coupled neural network , 2007, Pattern Recognit. Lett..

[5]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[6]  Wageeh W. Boles.  A Wavelet Transform Based Technique For The Recognition Of The Human Iris , 1996, Fourth International Symposium on Signal Processing and Its Applications.

[7]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[8]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[9]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[10]  Libor Masek,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2003 .

[11]  John Daugman,et al.  Probing the Uniqueness and Randomness of IrisCodes: Results From 200 Billion Iris Pair Comparisons , 2006, Proceedings of the IEEE.

[12]  Richard P. Wildes,et al.  A system for automated iris recognition , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[13]  Matthew N. Dailey,et al.  Iris recognition performance enhancement using weighted majority voting , 2008, 2008 15th IEEE International Conference on Image Processing.

[14]  Tieniu Tan,et al.  Iris recognition using circular symmetric filters , 2002, Object recognition supported by user interaction for service robots.

[15]  Yillbyung Lee,et al.  Iris recognition using collarette boundary localization , 2004, ICPR 2004.

[16]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.

[17]  Okhwan Byeon,et al.  Efficient Iris Recognition through Improvement of Feature Vector and Classifier , 2001 .

[18]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  S. Noh,et al.  MULTIRESOLUTION INDEPENDENT COMPONENT ANALYSIS FOR IRIS IDENTIFICATION , 2002 .

[20]  Ludmila I. Kuncheva,et al.  Generating classifier outputs of fixed accuracy and diversity , 2002, Pattern Recognit. Lett..

[21]  James R. Matey,et al.  Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments , 2006, Proceedings of the IEEE.

[22]  Tieniu Tan,et al.  Biometric personal identification based on iris patterns , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[23]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Luís A. Alexandre,et al.  Iris segmentation methodology for non-cooperative recognition , 2006 .

[25]  Bin Li,et al.  Iris Recognition Algorithm Using Modified Log-Gabor Filters , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[26]  Lionel Torres,et al.  Person Identification Technique Using Human Iris Recognition , 2002 .

[27]  Wei Cai,et al.  A region-based multi-sensor image fusion scheme using pulse-coupled neural network , 2006, Pattern Recognit. Lett..

[28]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.