Recently, face recognition systems have demonstrated remarkable performances and thus gained a vital role in our daily life. They already surpass human face verification accountability in many scenarios. However, they lack explanations for their predictions. Compared to human operators, typical face recognition network system generate only binary decisions without further explanation and insights into those decisions. This work focuses on explanations for face recognition systems, vital for developers and operators. First, we introduce a confidence score for those systems based on facial feature distances between two input images and the distribution of distances across a dataset. Secondly, we establish a novel visualization approach to obtain more meaningful predictions from a face recognition system, which maps the distance deviation based on a systematic occlusion of images. The result is blended with the original images and highlights similar and dissimilar facial regions. Lastly, we calculate confidence scores and explanation maps for several state-of-the-art face verification datasets and release the results on a web platform. We optimize the platform for a user-friendly interaction and hope to further improve the understanding of machine learning decisions. The source code is available on GitHub, and the web platform is publicly available at http://explainable-face-verification.ey.r.appspot.com.

翻译：近期，人脸识别系统展现了卓越的性能，进而在日常生活中发挥了关键作用。在许多场景中，它们已超越人类的人脸验证可靠度。然而，这些系统对其预测缺乏解释。相较于人类操作员，典型的人脸识别网络系统仅生成二元决策，而无法提供进一步的解释及对决策的深入洞察。本研究聚焦于人脸识别系统的可解释性，这对开发者和操作员至关重要。首先，我们基于两幅输入图像之间的人脸特征距离以及数据集上的距离分布，为这些系统引入了一个置信度分数。其次，我们建立了一种新颖的可视化方法，通过系统性地遮挡图像来映射距离偏差，从而从人脸识别系统中获得更有意义的预测。该结果与原始图像融合，并突出显示相似与不相似的面部区域。最后，我们在多个最先进的人脸验证数据集上计算置信度分数与解释映射图，并将结果发布在一个网络平台上。我们对该平台进行了优化以提升用户交互体验，并期望进一步增进对机器学习决策的理解。源代码已在GitHub上公开，网络平台亦可通过 http://explainable-face-verification.ey.r.appspot.com 公开访问。