Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss.

翻译：我们能否建立一个可以解释的面部识别网络,能够学习眼睛、鼻子、嘴等面部部分特征,而无需任何人工说明或补充数据集?在本文中,我们提议建立一个通用的可解释通道损失(ECLOS),以构建一个可以解释的面部识别网络。受过ECLOS培训的可解释网络可以很容易地了解目标相向层的面部部分代表,其中单个频道可以探测到一定的面部部分。我们对数十个数据集的实验表明,ECLOS实现了更高级的可解释度量,同时改善了面部核实的性能,而没有面部对齐。此外,我们的可视化结果还可以说明拟议的ECLOs的有效性。