Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their "black-box" nature. In recent years, studies have been carried out to give an interpretation of the decision of a deep FR system. However, the affinity between the input facial image and the extracted deep features has not been explored. This paper contributes to the problem of explainable face recognition by first conceiving a face reconstruction-based explanation module, which reveals the correspondence between the deep feature and the facial regions. To further interpret the decision of an FR model, a novel visual saliency explanation algorithm has been proposed. It provides insightful explanation by producing visual saliency maps that represent similar and dissimilar regions between input faces. A detailed analysis has been presented for the generated visual explanation to show the effectiveness of the proposed method.

翻译：尽管深度卷积神经网络在人脸识别任务中取得了巨大成功，但当前方法因其“黑箱”特性而缺乏对预测结果的可解释性。近年来，已有研究致力于解释深度人脸识别系统的决策过程，但输入人脸图像与提取的深度特征之间的关联性尚未得到探索。本文首先设计了一个基于人脸重建的解释模块，揭示深度特征与人脸区域之间的对应关系，从而推动可解释人脸识别问题的研究。为了进一步解释人脸识别模型的决策，提出了一种新颖的视觉显著性解释算法。该算法通过生成表示输入人脸之间相似与差异区域的视觉显著性图，提供具有洞察力的解释。本文对生成的视觉解释进行了详细分析，以展示所提方法的有效性。