Explainable Face Verification via Feature-Guided Gradient Backpropagation
<|reference_start|>Explainable Face Verification via Feature-Guided Gradient Backpropagation: Recent years have witnessed significant advancement in face recognition (FR) techniques, with their applications widely spread in people's lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such systems. Existing studies relying on various mechanisms have investigated the usage of saliency maps as an explanation approach, but suffer from different limitations. This paper first explores the spatial relationship between face image and its deep representation via gradient backpropagation. Then a new explanation approach FGGB has been conceived, which provides precise and insightful similarity and dissimilarity saliency maps to explain the "Accept" and "Reject" decision of an FR system. Extensive visual presentation and quantitative measurement have shown that FGGB achieves superior performance in both similarity and dissimilarity maps when compared to current state-of-the-art explainable face verification approaches.<|reference_end|>
@article{lu2024explainable,
    title={Explainable Face Verification via Feature-Guided Gradient
  Backpropagation},
    author={Yuhang Lu, Zewei Xu, and Touradj Ebrahimi},
    journal={arXiv preprint arXiv:2403.04549},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04549},
    primaryClass={cs.CV eess.IV}
}