João Brito, Vasco Lopes, Bruno Degardin, Hugo Proença
Understanding and justifying the decisions generated by automated recognizers has been a focal point of biometrics research, driven by the need for systems that are not only effective but transparent and accountable. Clear explanations improve user trust and system credibility, while addressing the growing concerns regarding the role of AI in our daily lives. In this work, we propose a framework that integrates biometric recognition with visual interpretations of the features that contribute the most to a match/non-match decision. Our method leverages adversarial generative models to create a set composed exclusively of ``genuine" image pairs. From these, the most similar candidates to a given query are identified and, assuming enough similarity in phase between the query and the retrieved pairs, the pixel-wise differences highlight the image regions that supported the decision. Comparative evaluations against established interpretability techniques (SHAP, LIME, and Saliency Maps) as well as other state-of-the-art fine-grained visual recognizers demonstrate that our framework delivers intuitive visual explanations without compromising recognition performance. These findings underscore our method's potential to enhance the transparency and credibility of biometric systems while maintaining high accuracy.