Robust Iris Recognition Using Advanced Correlation Techniques.

The iris is considered one of the most reliable and stable biometrics as it is believed to not change significantly during a person's lifetime. Standard techniques for iris recognition, popularized by Daugman, apply Gabor wavelet analysis for feature extraction. In this paper, we consider an alternative method for iris recognition, the use of advanced distortion-tolerant correlation filters for robust pattern matching. These filters offer two primary advantages: shift invariance, and the ability to tolerate within-class image variations. The iris images we use in our experiments are from the CASIA database and also from an iris database we collected at CMU. In this paper, we perform automatic segmentation of the iris (which surrounds the pupil) from the rest of the eye, normalizing for scale and pupil dilation. We then use these segmented iris images to compare the recognition performance of various methods, including Gabor wavelet feature extraction, to correlation filters. [ABSTRACT FROM AUTHOR]