面向人脸识别的口罩区域修复算法

遮挡下的人脸识别一直是现实场景中的一个难题。特别是新冠肺炎疫情爆发后，在机场、车站等需要鉴别入场人员身份信息的场所，口罩遮挡使得可供识别的面部特征大幅减少，原有的人脸识别算法准确率随之下降。对去除口罩遮挡进行了研究，提出了一个新的框架修复人脸，利用边缘生成网络还原遮挡区域的边缘，在此基础上再利用区域填充网络恢复被遮挡的人脸，同时保留身份信息。为提升模型的性能，提出空间加权对抗损失和身份一致性损失训练上述网络，并利用关键点信息，构建了两个戴口罩的人脸数据集。实验结果表明，恢复被口罩遮挡的人脸的图像使人脸识别算法 ArcFace 的准确率达到 98.39%，比直接采用ArcFace识别遮挡人脸提升了4.13%的准确率。

Region inpainting algorithm of mouth-muffles for facial recognition

Facial recognition under occlusion is a well-known difficult problem in real scenes.Especially after the outbreak of COVID-19, in airports, stations and other places that need to verify the identity of visitors, the mouth-muffle occlusion greatly reduces the facial features that can be important for identification, and the accuracy of face recognition algorithm decreases.Face de-occlusion was studied, and a novel framework was proposed to restore face, which used the edge generation network to generate edge maps.On this basis, the occluded region was restored by the region completion network while preserving identity information.In order to improve the performance, the spatial weighted adversarial loss and identity-preserving loss were introduced to train the above two networks.Then two face datasets with mouth-muffles were constructed by face landmarks.The experimental results show that the accuracy of facial recognition algorithm ArcFace on the face dataset restored by the proposed model was 98.39%, which was 4.13% higher than that of directly using ArcFace.