Deep Adversarial Frameworks for Visually Explainable Periocular Recognition - Brito, João Pedro da Cruz (2022)
Machine Learning (ML) models have pushed stateoftheart performance closer to (and even beyond) human level. However, the core of such algorithms is usually latent and hardly understandable. Thus, the field of Explainability focuses on researching and adopting techniques that can explain the reasons that support a model’s predictions. Such explanations of the decisionmaking process would help to build trust between said model and the human(s) using it. An explainable system also allows for better debugging, during the training phase, and fixing, upon deployment. But why should a developer devote time and effort into refactoring or rethinking Artificial Intelligence (AI) systems, to make them more transparent? Don’t they work just fine? Despite the temptation to answer ”yes”, are we really considering the cases where these systems fail? Are we assuming that ”almost perfect” accuracy is good enough? What if, some of the cases where these systems get it right, were just a small margin away from a complete miss? Does that even matter? Considering the evergrowing presence of ML models in crucial areas like forensics, security and healthcare services, it clearly does. Motivating these concerns is the fact that powerful systems often operate as blackboxes, hiding the core reasoning underneath layers of abstraction [Gue]. In this scenario, there could be some seriously negative outcomes if opaque algorithms gamble on the presence of tumours in Xray images or the way autonomous vehicles behave in traffic. It becomes clear, then, that incorporating explainability with AI is imperative. More recently, the politicians have addressed this urgency through the General Data Protection Regulation (GDPR) [Com18]. With this document, the European Union (EU) brings forward several important concepts, amongst which, the ”right to an explanation”. The definition and scope are still subject to debate [MF17], but these are definite strides to formally regulate the explainable depth of autonomous systems. Based on the preface above, this work describes a periocular recognition framework that not only performs biometric recognition but also provides clear representations of the features/regions that support a prediction. Being particularly designed to explain nonmatch (”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 elementwise 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 stateoftheart, while adding visually pleasing explanations.