Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two faces images are matched or not matched by a given face recognition system is important to operators, users, anddevelopers to increase trust, accountability, develop better systems, and highlight unfair behavior. In this work, we propose xSSAB, an approach to back-propagate similarity score-based arguments that support or oppose the face matching decision to visualize spatial maps that indicate similar and dissimilar areas as interpreted by the underlying FR model. Furthermore, we present Patch-LFW, a new explainable face verification benchmark that enables along with a novel evaluation protocol, the first quantitative evaluation of the validity of similarity and dissimilarity maps in explainable face recognition approaches. We compare our efficient approach to state-of-the-art approaches demonstrating a superior trade-off between efficiency and performance. The code as well as the proposed Patch-LFW is publicly available at: https://github.com/marcohuber/xSSAB.

翻译：可解释人脸识别技术正随着其在安全关键型应用中的广泛部署而日益受到关注。理解人脸识别系统为何将两幅人脸图像判定为匹配或不匹配，对于操作人员、用户和开发者而言至关重要，这有助于增强信任、提升问责性、开发更优系统以及揭示不公平行为。本文提出xSSAB方法，该方法通过反向传播支持或反对人脸匹配决策的相似度评分参数，生成空间热力图，直观展示底层人脸识别模型所判定的相似区域与差异区域。此外，我们构建了新型可解释人脸验证基准Patch-LFW，并配套提出全新评估协议，首次实现可解释人脸识别方法中相似性/差异性热力图有效性的定量评估。实验表明，我们提出的高效方法在效率与性能之间取得了优于现有技术的平衡。相关代码及Patch-LFW数据集已开源：https://github.com/marcohuber/xSSAB。