In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. 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 the usage of visual saliency maps as an explanation, but they often lack a discussion and analysis in the context of face recognition. This paper concentrates on explainable face verification tasks and conceives a new explanation framework. Firstly, a definition of the saliency-based explanation method is provided, which focuses on the decisions made by the deep FR model. Secondly, a new model-agnostic explanation method named CorrRISE is proposed to produce saliency maps, which reveal both the similar and dissimilar regions of any given pair of face images. Then, an evaluation methodology is designed to measure the performance of general visual saliency explanation methods in face verification. Finally, substantial visual and quantitative results have shown that the proposed CorrRISE method demonstrates promising results in comparison with other state-of-the-art explainable face verification approaches.

翻译：过去几年中，深度卷积神经网络在验证与识别场景下持续推动人脸识别技术的发展前沿。尽管取得高精度，这类技术常因缺乏可解释性而受到批评。对理解深度人脸识别系统决策过程的需求日益增长。近期研究探索了视觉显著性图作为解释工具的使用，但往往缺乏从人脸识别视角的深度讨论与分析。本文聚焦于可解释的人脸验证任务，构思了一个全新的解释框架。首先，我们给出了基于显著性的解释方法定义，该方法重点关注深度人脸识别模型的决策过程。其次，提出了一种新型模型无关的解释方法CorrRISE，该算法能够生成揭示任意一对人脸图像中相似区域与差异区域的显著性图。随后，设计了评估方法论以衡量通用视觉显著性解释方法在人脸验证任务中的性能。最后，大量视觉化与量化结果表明，与当前最先进的可解释人脸验证方法相比，所提出的CorrRISE方法展现出显著优势。