Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis

Abstract

Accurate iris detection is a crucial part of an iris recognition system. One of the main issues in iris segmentation is coping with occlusion that happens due to eyelids and eyelashes. In the literature, some various methods have been suggested to solve the occlusion problem. In this paper, two different segmentations of iris are presented. In the first algorithm, a circle is located around the pupil with an appropriate diameter. The iris area encircled by the circular boundary is used for recognition purposes then. In the second method, again a circle is located around the pupil with a larger diameter. This time, however, only the lower part of the encircled iris area is utilized for individual recognition. Wavelet-based texture features are used in the process. Hamming and harmonic mean distance classifiers are exploited as a mixed classifier in suggested algorithm. It is observed that relying on a smaller but more reliable part of the iris, though reducing the net amount of information, improves the overall performance. Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31%. The sensitivity of the proposed method is analyzed versus contrast, illumination, and noise as well, where lower sensitivity to all factors is observed when the lower half of the iris is used for recognition.