Ocular Biometrics with Low-Resolution Images Based on Ocular Super-Resolution CycleGAN

Abstract

Iris recognition, which is known to have outstanding performance among conventional biometrics techniques, requires a high-resolution camera and a sufficient amount of lighting to capture images containing various iris patterns. To address these issues, research is actively conducted on ocular recognition to include a periocular region in addition to the iris region, which also requires a high-resolution camera to capture images, indicating limited applications due to costs and size limitation. Accordingly, this study proposes an ocular super-resolution cycle-consistent generative adversarial network (OSRCycleGAN) for ocular super-resolution reconstruction, and additionally proposes a method to improve recognition performance in case that ocular images are acquired at a low-resolution. The results of the experiment conducted using open databases, namely, CASIA-iris-Distance and Lamp v4, and IIT Delhi iris database, showed that the equal error rate of recognition of the proposed method was 3.02%, 4.06% and 2.13% for each database, respectively, which outperformed state-of-the-art methods.