  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.
