9. 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.