Explainable Face Verification via Feature-Guided Gradient
  Backpropagation
Yuhang Lu Zewei Xu Touradj Ebrahimi
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.