19 research outputs found
Deep Adversarial Frameworks for Visually Explainable Periocular Recognition
Machine Learning (ML) models have pushed stateÂofÂtheÂart 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 decisionÂmaking 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 everÂgrowing 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 blackÂboxes,
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 XÂray 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 nonÂmatch
(”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 elementÂwise 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
stateÂofÂtheÂart, while adding visually pleasing explanations