Mobile periocular matching with pre-post cataract surgery

TLDR: Periocular recognition with pre and post cataract surgery images is investigated with a mobile periocular database of 145 subjects to establish a framework that achieves over 69% rank-10 accuracy and around 24% genuine accept rate at 1% false accept rate in inter-session experiments.

Abstract: Ocular recognition algorithms, including iris matching, have been used in several applications including large scale national ID projects such as India's Aadaar. Deployment of large-scale biometric systems is expected to rely on using multiple devices including mobile devices to ensure widespread adoption of biometric recognition systems. Ocular images captured using mobile devices may have challenges such as uncontrolled illumination, complex background, and geometric distortions. Further, among many enrollees of large scale biometrics program, some may have ocular diseases. One of the most common ocular disease in elderly is cataract. While it is established that iris recognition may be challenging due to ocular diseases, this paper investigates periocular recognition with pre and post cataract surgery images. In this research, we present a mobile periocular database of 145 subjects1. Baseline results also include a framework that achieves over 69% rank-10 accuracy and around 24% genuine accept rate at 1% false accept rate in inter-session experiments.