Multi-frame Super Resolution for Ocular Biometrics

Some biometrics methods, especially ocular, may use fine spatial information akin to level-3 features. Examples include fine vascular patterns visible in the white of the eyes in green and blue channels, iridial patterns in near infrared, or minute periocular features in visible light. In some mobile applications, an NIR or RGB camera is used to capture these ocular images in a "selfie" like manner. However, most of such ocular images captured under unconstrained environments are of lower quality due to spatial resolution, noise, and motion blur, affecting the performance of the ensuing biometric authentication. Here we propose a multi-frame super resolution (MFSR) pipeline to mitigate the problem, where a higher resolution image is generated from multiple lower resolution, noisy and blurry images. We show that the proposed MFSR method at 2× upscaling can improve the equal error rate (EER) by 9.85% compared to single frame bicubic upscaling in RGB ocular matching while being up to 8.5× faster than comparable state-of-the-art MFSR method.