  Over recent years, deep convolutional neural networks have significantly
advanced the field of face recognition techniques for both verification and
identification purposes. Despite the impressive accuracy, these neural networks
are often criticized for lacking explainability. There is a growing demand for
understanding the decision-making process of AI-based face recognition systems.
Some studies have investigated the use of visual saliency maps as explanations,
but they have predominantly focused on the specific face verification case. The
discussion on more general face recognition scenarios and the corresponding
evaluation methodology for these explanations have long been absent in current
research. Therefore, this manuscript conceives a comprehensive explanation
framework for face recognition tasks. Firstly, an exhaustive definition of
visual saliency map-based explanations for AI-based face recognition systems is
provided, taking into account the two most common recognition situations
individually, i.e., face verification and identification. Secondly, a new
model-agnostic explanation method named CorrRISE is proposed to produce
saliency maps, which reveal both the similar and dissimilar regions between any
given face images. Subsequently, the explanation framework conceives a new
evaluation methodology that offers quantitative measurement and comparison of
the performance of general visual saliency explanation methods in face
recognition. Consequently, extensive experiments are carried out on multiple
verification and identification scenarios. The results showcase that CorrRISE
generates insightful saliency maps and demonstrates superior performance,
particularly in similarity maps in comparison with the state-of-the-art
explanation approaches.
