Learning Deeply Coupled Autoencoders For Smartphone Based Robust Periocular Verification

Smartphone based periocular recognition has received substantial attention from the biometric research community. In this work, we propose a new scheme for the smartphone based periocular recognition. The proposed scheme is based on the texture features extracted from the periocular images using Maximum Response (MR) filters. These texture features are then classified using a deep neural network based on deeply coupled autoencoders. Extensive experiments are carried out on the large-scale VISOB database with 550 subjects captured using three different smartphones. The obtained results demonstrate the average performance of the proposed scheme with GMR of over 92% at FMR = 10(-3).