Relativistic Discriminator: A One-Class Classifier for Generalized Iris Presentation Attack Detection

Abstract

Iris based recognition systems are vulnerable to presentation attacks (PAs) where artifacts such as cosmetic contact lenses, artificial eyes and printed eyes can be used to fool the system. While many learning-based algorithms have been proposed to detect such attacks, very few are equipped to handle previously unseen or newly constructed PAs. In this research, we propose a presentation attack detection (PAD) method that utilizes a discriminator that is trained to distinguish between bonafide iris images and synthetically generated iris images. We hypothesize that such a discriminator will generate a tight boundary around the bonafide samples. This would allow the discriminator to better separate the bonafide samples from all types of PA samples. For generating synthetic irides, we train the Relativistic Average Standard Generative Adversarial Network (RaSGAN) that has been shown to generate higher resolution and better quality images than standard GANs. The relativistic discriminator (RD) component of the trained RaS-GAN is then appropriated for PA detection and is referred to as RD-PAD. Experimental results convey the efficacy of the RD-PAD as a one-class anomaly detector.