Explainable face recognition is the problem of explaining why a facial matcher matches faces. In this paper, we provide the first comprehensive benchmark and baseline evaluation for explainable face recognition. We define a new evaluation protocol called the ``inpainting game'', which is a curated set of 3648 triplets (probe, mate, nonmate) of 95 subjects, which differ by synthetically inpainting a chosen facial characteristic like the nose, eyebrows or mouth creating an inpainted nonmate. An explainable face matcher is tasked with generating a network attention map which best explains which regions in a probe image match with a mated image, and not with an inpainted nonmate for each triplet. This provides ground truth for quantifying what image regions contribute to face matching. Furthermore, we provide a comprehensive benchmark on this dataset comparing five state of the art methods for network attention in face recognition on three facial matchers. This benchmark includes two new algorithms for network attention called subtree EBP and Density-based Input Sampling for Explanation (DISE) which outperform the state of the art by a wide margin. Finally, we show qualitative visualization of these network attention techniques on novel images, and explore how these explainable face recognition models can improve transparency and trust for facial matchers.

本文针对可解释脸部识别问题，提出了一个名为“inpainting game”的评估协议，并提供了一套基准数据集用于比较5种最先进的面部Attention算法。同时，展示了这些算法在新颖的图像上的可视化效果，并探讨了这些算法如何提高脸部匹配器的透明度和可信度。

可解释的人脸识别