关键词: 人脸识别, 课程式学习, 图像质量, 自适应损失

Abstract: In recent years, the sample mining strategy has been integrated into the loss function of face recognition, significantly improving the performance of face recognition. But most of the work focuses on how to mine difficult samples during the training phase, without considering the potential unrecognized sample images in the difficult samples, resulting in poor recognition performance of the model for low-quality facial images. To solve this problem, this paper proposes a hybrid adaptive loss function MixFace that combines sample difficulty adaptation and image quality adaptation. The loss function combines the CurricularFace based on curriculum learning with the image adaptive loss function AdaFace. The feature norm is incorporated into the loss function as an image quality indicator. On the premise of focusing on image quality, this paper focuses on simple samples in the early training stage and difficult samples in the later training stage, reducing the network model’s attention to some low-quality unrecognized samples in difficult samples. Trained on CASIA-WebFace and MS1MV2 datasets, MixFace shows varying degrees of performance improvement compared with CurricularFace and AdaFace on high-quality test sets LFW, CFP_FP, AgeDB, CALFW, and CPLFW. At the same time, MixFace shows better recognition performance than CurricularFace and AdaFace on medium quality test sets IJB-B, IJB-C and low-quality test set TinyFace. Experimental results show that MixFace can effectively reduce the interference of unrecognized images, thereby improving the performance of low-quality face recognition. At the same time, benefiting from the curriculum learning method in MixFace, it can still maintain good performance for high-quality face recognition.

Key words: face recognition, curriculum learning, image quality, adaptive loss