  We study the XAI (explainable AI) on the face recognition task, particularly
the face verification here. Face verification is a crucial task in recent days
and it has been deployed to plenty of applications, such as access control,
surveillance, and automatic personal log-on for mobile devices. With the
increasing amount of data, deep convolutional neural networks can achieve very
high accuracy for the face verification task. Beyond exceptional performances,
deep face verification models need more interpretability so that we can trust
the results they generate. In this paper, we propose a novel similarity metric,
called explainable cosine ($xCos$), that comes with a learnable module that can
be plugged into most of the verification models to provide meaningful
explanations. With the help of $xCos$, we can see which parts of the two input
faces are similar, where the model pays its attention to, and how the local
similarities are weighted to form the output $xCos$ score. We demonstrate the
effectiveness of our proposed method on LFW and various competitive benchmarks,
resulting in not only providing novel and desiring model interpretability for
face verification but also ensuring the accuracy as plugging into existing face
recognition models.
