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.

翻译：近年来，人脸识别技术在人们生活和安全敏感领域的广泛应用使其取得了显著进展。对于此类系统决策的可靠解释需求日益增长。现有研究依赖多种机制探索了使用显著性图作为解释方法，但存在不同局限。本文首先通过梯度反向传播探索人脸图像与其深度表示之间的空间关系，进而提出了一种新的解释方法FGGB，该方法能够生成精确且富有洞察力的相似性及差异性显著性图，以解释人脸识别系统的"接受"与"拒绝"决策。大量可视化呈现与定量测量表明，与当前最先进的可解释人脸验证方法相比，FGGB在相似性图与差异性图上均取得了更优性能。