Periocular biometric recognition using image sets

Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired non-intrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification. Current techniques have focused on choosing a single best frame, mostly manually, for matching. In contrast, we formulate, for the first time, person identification based on periocular regions as an image set classification problem. We generate periocular region image sets from the Multi Bio-metric Grand Challenge (MBGC) NIR videos. Periocular regions of the right eyes are mirrored and combined with those of the left eyes to form an image set. Each image set contains periocular regions of a single subject. For imageset classification, we use six state-of-the-art techniques and report their comparative recognition and verification performances. Our results show that image sets of periocular regions achieve significantly higher recognition rates than currently reported in the literature for the same database.