Biometric personal identification based on iris recognition using complex wavelet transforms

A new iris recognition system based on complex wavelet Transforms is described. In this work iris recognition based on Gabor wavelet and Morlet wavelet are described. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris is encoded into a compact sequence of 2-D wavelet coefficients, which generate an ldquoiris coderdquo of 4096-bits. The statistical parameters like mean and covariance of coefficients of the iris images are also computed. Two different iris codes are compared using exclusively OR comparisons. Also the new iris pattern is compared against the stored pattern after computing the probability of new iris pattern and identification is performed.

[1]  Yingzi Du,et al.  Iris pattern extraction using bit planes and standard deviations , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

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

[3]  Jie Wang,et al.  Iris Feature Extraction Based on Wavelet Packet Analysis , 2006, 2006 International Conference on Communications, Circuits and Systems.

[4]  Mariusz Zubert,et al.  Iris Recognition Algorithm Optimized for Hardware Implementation , 2006, 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

[5]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

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

[7]  Jamal Ahmad Dargham,et al.  Iris recognition using self-organizing neural network , 2002, Student Conference on Research and Development.

[8]  John Daugman.  How iris recognition works , 2004 .

[9]  Lu Zhaoyang,et al.  Efficient iris recognition by computing discriminable textons , 2005, 2005 International Conference on Neural Networks and Brain.