Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domain

Marco Huber, Naser Damer; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1016-1026

The need for more transparent face recognition (FR) along with other visual-based decision-making systems has recently attracted more attention in research society and industry. The reasons why two face images are matched or not matched by a deep learning-based face recognition system are not obvious due to the high number of parameters and the complexity of the models. However it is important for users operators and developers to ensure trust and accountability of the system and to analyze drawbacks such as biased behavior. While many previous works use spatial semantic maps to highlight the regions that have a significant influence on the decision of the face recognition system frequency components which are also considered by CNNs are neglected. In this work we take a step forward and investigate explainable face recognition in the unexplored frequency domain. This makes this work the first to propose explainability of verification-based decisions in the frequency domain thus explaining the relative influence of the frequency components of each input toward the obtained outcome. To achieve this we manipulate face images in the spatial frequency domain and investigate the impact on verification outcomes. In extensive quantitative experiments along with investigating two special scenarios cases cross-resolution FR and morphing attacks (the latter in supplementary material) we observe the applicability of our proposed frequency-based explanations.