A Deep Adversarial Framework for Visually Explainable Periocular Recognition

Abstract

In the biometrics context, the ability to provide the reasoning behind a decision has been at the core of major research efforts. Explanations serve not only to increase the trust amongst the users of a system, but also to augment the system’s overall accountability and transparency. In this work, we describe a periocular recognition frame-work that not only performs biometric recognition, but also provides visual representations of the features/regions that supported a decision. Being particularly designed to explain non-match ("impostors") decisions, our solution uses adversarial generative techniques to synthesise a large set of "genuine" image pairs, from where the most similar elements with respect to a query are retrieved. Then, assuming the alignment between the query/retrieved pairs, the element-wise differences between the query and a weighted average of the retrieved elements yields a visual explanation of the regions in the query pair that would have to be different to transform it into a "genuine" pair. Our quantitative and qualitative experiments validate the proposed solution, yielding recognition rates that are similar to the state-of-the-art, but - most importantly - also providing the visual explanations for every decision.