A study of how gaze angle affects the performance of iris recognition

In traditional iris recognition systems, majority of researches improves the recognition performance for frontal images by ignoring the challenging issues including corneal refraction, 3D iris texture, limbus occlusion, and blur. When comparing images from the same angle such as frontal, all the challenging effects have similar distortions (i.e., corneal refraction and 3D texture) or minimal effects (i.e., limbus effect and blur) on all iris images. However, in off-angle iris recognition they have a significant negative impact on the accuracy and performance and they require additional treatments. In this paper, we first investigate how eye structures related iris recognition affects the performance of iris biometrics for different gaze angles and then quantify the effect of gaze angle on the inter-class and intra-class Hamming distance distributions. Based on our results from real images, as gaze angle of the probe image increases, the Hamming distance scores increases in intra-class distribution. We further found that average Hamming distance of the inter-class decreases as image captured from stepper angles and the distribution moves towards the intra-class distribution that causes an increment in the false-match-rate due to the existence of less area in the segmented off-angle images compared with the frontal images.