Abstract: Existing face recognition methods cannot effectively eliminate the influence of corrupted features caused by occlusion. As the features flow deeper, the corrupted features get entangled with the effective features used for identity classification, which affects the recognition results. To address the problem, this paper designs an occluded face recognition method based on segmentation and multi-stage mask learning strategy. The model consists of three components: occlusion detection and segmentation, feature extraction, and mask learning unit. The proposed method only needs one end-to-end process to learn feature masks and deep occlusion-robust features without relying on additional occlusion detectors. The mask learning units take different sizes of occlusion segmentation representations and facial features of different stages as input, generate corresponding feature masks for different stages of feature extraction, and effectively eliminate the influence of corrupted features caused by occlusion at each stage of feature extraction through mask operations. Finally, a feature pyramid is constructed to fuse features of different stages for identity classification. Experimental results show that the proposed method can effectively improve the accuracy of occluded face recognition. The accuracy on the occluded LFW dataset and the real masked datasets MFR2 and  Mask_whn reach 98.77%, 96.70% and 81.53%, respectively, which has an accuracy improvement of 2.04, 0.48 and 4.44 percentage points compared with the existing mainstream methods.

Key words: occluded face recognition, multi-stage mask learning, occlusion detection and segmentation, feature pyramid

摘要： 现有的人脸识别方法无法有效消除遮挡造成的损坏特征的影响，随着网络层数加深，损坏特征与用于身份分类的有效特征变得难以分离，影响识别结果。针对上述问题，设计了一种基于分割和多级掩膜学习策略的遮挡人脸识别方法，模型由遮挡检测分割、特征提取、掩膜学习单元三大模块构成，无需依赖额外的遮挡检测器，且无论训练还是测试都只需要一次端到端的过程即可同时学习特征掩膜和深度抗遮挡特征。掩膜学习单元以不同尺寸的遮挡分割表示和不同阶段的人脸特征为输入，为特征提取的不同阶段生成对应的掩膜，通过掩膜运算在特征提取的各阶段有效消除遮挡造成的损坏特征的影响，最终构建特征金字塔融合各阶段特征进行身份分类。实验结果表明该方法可有效提高遮挡人脸识别的准确率，在经过遮挡处理的LFW数据集以及真实的口罩遮挡数据集MFR2、Mask_whn上的准确率分别达到了98.77%、96.70%、81.53%，与现有的主流方法相比分别提升了2.04、0.48、4.44个百分点。

关键词: 遮挡人脸识别, 多级掩膜学习, 遮挡检测分割, 特征金字塔