Fast Iris Detection Via Shape Based Circularity

We define an iris detection algorithm that performs an order of magnitude faster than the state of the art, while preserving accuracy. The algorithm isolates the pupil boundary by extracting image edges, then finding the largest contiguous set of points that satisfy the circularity criterion and contain mostly dark pixels. The iris/sclera boundary is found by horizontally and simultaneously searching along both directions of the pupil center for the highest cumulative difference in intensities. Current detection systems mainly rely on the methods proposed by [D] in order to isolate the iris pattern reliably, yet they are computationally expensive and expectedly slow. We apply a measure of circularity to isolate both the sclera and pupil boundaries, avoiding the exhaustive search required by [D]. Our method correctly identifies the iris region in 95% of test cases in the CASIA 3 dataset [CA]. While the detection rate is slightly lower compared to competitors, our method performs in O(n(2)) time compared to Omega(n(3)) that [D] offers. The iris detection procedure of [D], implemented in Matlab runs an average of roughly 15 seconds per image, while our own implementation in C++ on a single core 2.0 Ghz processor takes about 5 milliseconds on the same system, which is also faster than [HAK].