Explainable Face Verification via Feature-Guided Gradient Backpropagation

Recent years have witnessed significant advancement in face recognition (FR) techniques, with their applications impacting people's lives including in security-sensitive areas. There is a growing need for reliable interpretation 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 called Feature-Guided Gradient Backpropagation (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 comparable results in similarity maps and superior performance in dissimilarity maps when compared to current state-of-the-art explainable face verification approaches.