Application of Correlation Filters for Iris Recognition

Excellent recognition accuracies have been reported when using iris images, particularly when high-quality iris images can be acquired. The best-known strategy for matching iris images requires segmenting the iris from the background, converting the segmented iris image from Cartesian coordinates to polar coordinates, using Gabor wavelets to obtain a binary code to represent that iris, and using the Hamming distances between such binary representations to determine whether two iris images match or do not match. However, some of the component operations may not work well when the iris images are of poor quality, perhaps as a result of the long distance between the camera and the subject. One approach to matching images with appearance variations is the use of correlation filters (CF). In this chapter, we discuss the use of CFs for iris recognition. CFs exhibit important benefits such as shift-invariance and graceful degradation and have proven worthy of consideration in other pattern recognition applications such as automatic target recognition. In this chapter, we will discuss the basics of CF design and show how CFs can be used for iris segmentation and matching.

[1]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[2]  B. V. K. Vijaya Kumar,et al.  A Bayesian Approach to Deformed Pattern Matching of Iris Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Marios Savvides,et al.  Correlation Pattern Recognition for Face Recognition , 2006, Proceedings of the IEEE.

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

[5]  Brendan J. Frey,et al.  Graphical Models for Machine Learning and Digital Communication , 1998 .

[6]  B. V. K. Vijaya Kumar,et al.  Correlation Pattern Recognition: References , 2005 .

[7]  B. V. K. Vijaya Kumar,et al.  Correlation Pattern Recognition , 2002 .

[8]  B. V. K. Vijaya Kumar,et al.  Robust Iris Recognition Using Advanced Correlation Techniques , 2005, ICIAR.

[9]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.

[10]  P. Réfrégier.  Filter design for optical pattern recognition: multicriteria optimization approach. , 1990, Optics letters.

[11]  Abhijit Mahalanobis,et al.  Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set , 1999, Defense, Security, and Sensing.

[12]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  B. V. K. Vijaya Kumar,et al.  Iris Verification Using Correlation Filters , 2003, AVBPA.

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

[15]  B. V. Vijaya Kumar,et al.  Minimum-variance synthetic discriminant functions , 1986 .

[16]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.