Collaborative representation of deep sparse filtered features for robust verification of smartphone periocular images

Abstract

Ocular recognition on smartphone authentication applications are gaining popularity in academic research and in the commercial sector where operators are requesting reliable and robust biometric authentication. The wide acceptance of such ocular based authentication systems also depends on the verification performance on large scale testing with different data subject ethnic groups and platforms. In this work, we evaluate such a large database of ocular images collected using three different phones. Further, we benchmark the verification performance and propose a new framework to improve it. The proposed framework is based on collaboratively represented features from deep sparse filtering. We obtain a verification performance of Genuine Match Rate (GMR) of 97.56% at a False Match Rate (FMR) of 0.001% for periocular images obtained from a Samsung device. The overall performance of around 95% GMR at a FMR of 0.001% not only indicates the robust nature of the proposed framework, but also illustrates the efficacy in applying them for real-life authentication scenarios.