A Large-scale Database for Less Cooperative Iris Recognition

Abstract

Since the outbreak of the COVID-19 pandemic, iris recognition has been used increasingly as contactless and unaffected by face masks. Although less user cooperation is an urgent demand for existing systems, corresponding manually annotated databases could hardly be obtained. This paper presents a large-scale database of near-infrared iris images named CASIA-Iris-Degradation Version 1.0 (DV1), which consists of 15 subsets of various degraded images, simulating less cooperative situations such as illumination, off-angle, occlusion, and nonideal eye state. A lot of open-source segmentation and recognition methods are compared comprehensively on the DV1 using multiple evaluations, and the best among them are exploited to conduct ablation studies on each subset. Experimental results show that even the best deep learning frameworks are not robust enough on the database, and further improvements are recommended for challenging factors such as half-open eyes, off-angle, and pupil dilation. Therefore, we publish the DV1 with manual annotations online to promote iris recognition.