论文标题
可解释的面部识别的激活模板匹配损失
Activation Template Matching Loss for Explainable Face Recognition
论文作者
论文摘要
我们能否构建一个可解释的面部识别网络，能够学习基于面部部分的功能，例如眼睛，鼻子，嘴巴等，而无需任何手动注释或加法数据集？在本文中，我们提出了一个通用的可解释的通道损失（ECLOSS）来构建可解释的面部识别网络。经过Ecloss训练的可解释网络可以轻松地学习目标卷积层的基于面部的表示，单个通道可以检测到某个面部部分。我们对数十个数据集的实验表明，Ecloss实现了卓越的解释性指标，同时提高了面部验证的性能而无需面部对齐。此外，我们的可视化结果还说明了提出的ecloss的有效性。
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.
