Deep convolutional neural networks have been pushing the frontier of face
recognition (FR) techniques in the past years. Despite the high accuracy, they
are often criticized for lacking explainability. There has been an increasing
demand for understanding the decision-making process of deep face recognition
systems. Recent studies have investigated using visual saliency maps as an
explanation, but they often lack a discussion and analysis in the context of
face recognition. This paper conceives a new explanation framework for face
recognition. It starts by providing a new definition of the saliency-based
explanation method, which focuses on the decisions made by the deep FR model.
Then, a novel correlation-based RISE algorithm (CorrRISE) is proposed to
produce saliency maps, which reveal both the similar and dissimilar regions of
any given pair of face images. Besides, two evaluation metrics are designed to
measure the performance of general visual saliency explanation methods in face
recognition. Consequently, substantial visual and quantitative results have
shown that the proposed method consistently outperforms other explainable face
recognition approaches.

该论文提出了一种新的人脸识别解释框架，使用基于视觉显著性的解释方法来揭示深度人脸识别模型的决策过程；并提出一种新的相关性算法（CorrRISE）来生成显著性图，可同时揭示任意给定一对人脸图像中的相似和不同区域，并设计了两个评估指标来评估人脸识别中通用视觉显著性解释方法的性能， 实验证明该方法在解释人脸识别方面优于其他方法。