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.

翻译：深度卷积神经网络近年来不断推动人脸识别技术的前沿发展。尽管识别精度较高，但这些模型常因缺乏可解释性而受到批评。当前对理解深度人脸识别系统决策过程的需求日益增长。已有研究尝试使用视觉显著性图作为解释手段，但往往缺少针对人脸识别场景的系统讨论与分析。本文提出了一种面向人脸识别的新型解释框架。该框架首先重新定义了基于显著性的解释方法，聚焦于深度人脸识别模型的决策过程；随后提出了一种基于相关性的RISE算法（CorrRISE），通过生成显著性图揭示任意人脸图像对的相似区域与差异区域。此外，本文还设计了两种评估指标，用于衡量通用视觉显著性解释方法在人脸识别中的表现。大量的视觉与量化结果表明，所提方法在性能上持续优于其他可解释人脸识别方法。