Deep supervised class encoding for iris presentation attack detection

Abstract

The vulnerabilities of a Biometric Authentication System (BAS) to spoof attacks have gained substantial attention over the years. Iris recognition, one of the most reliable and accurate BASs is vulnerable to various kinds of presentation attacks such as by printed iris or contact lenses. The presentation attack detection (PAD) becomes even more challenging when an attacker imposes variations by carrying out multiple spoofing attacks. To address this challenge, an end-to-end Deep Supervised Class Encoding (DSCE) approach for iris-PAD is proposed in this paper. This is to deal with three main attacks by (i) printed iris images, (ii) contact lenses, and (iii) synthetically generated iris images. DSCE is an autoencoder based supervised feature learning approach that exploits the class information, and minimizes the reconstruction and classification errors simultaneously during the training phase. DSCE is employed to design an iris-PAD framework termed as DeepI, to perceive counterfeit access to an iris-BAS. Experimental results on different benchmark databases show that DSCE based DeepI outperforms the current state-of-the-art iris-PAD methods. Also, in cross-database training-testing settings, the proposed approach manifests a promising generalization capability.