Reliable iris localization using Hough transform, histogram-bisection, and eccentricity

The iris technology recognizes individuals from their iris texture with great precision. However, it does not perform well for the non-ideal data, where the eye image may contain non-ideal issues such as the off-axis eye image, blurring, non-uniform illumination, hair, glasses, etc. It is because of their iris localization algorithms, which are developed for the ideal data. In this paper, we propose a reliable iris localization algorithm. It includes localizing a coarse iris location in the eye image using the Hough transform and image statistics; localizing the pupillary boundary using a bi-valued adaptive threshold and the two-dimensional (2D) shape properties; localizing the limbic boundary by reusing the Hough accumulator and image statistics; and finally, regularizing these boundaries using a technique based on the Fourier series and radial gradients. The proposed technique is tested on the public iris databases: CASIA V1, CASIA-IrisV3-Lamp, CASIA-IrisV4-Thousand, IITD V1.0, MMU V1.0, and MMU (new) V2.0. Experimental results obtained on these databases show superiority of the proposed technique over some state of the art iris localization techniques.

Figure optionsDownload as PowerPoint slideHighlights
► Robust iris region localization in the eye image using Hough transform and image gray statistics.
► Pupillary boundary localization using a bi-valued adaptive threshold and 2D-shape properties.
► Fast limbic boundary localization by reusing Hough accumulator and image gray statistics.
► Conversion of the circular iris contours to non-circular contours.
► Tolerance to off-axis eye images, glasses, contact lenses, eyelashes, eyelids, and hair.