The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, MagFace introduces an adaptive mechanism to learn a wellstructured within-class feature distributions by pulling easy samples to class centers while pushing hard samples away. This prevents models from overfitting on noisy low-quality samples and improves face recognition in the wild. Extensive experiments conducted on face recognition, quality assessments as well as clustering demonstrate its superiority over state-of-the-arts. The code is available at https://github.com/IrvingMeng/MagFace.

本文提出了一种名为MagFace的损失函数，该函数可学习到一个通用的特征嵌入，其大小可以测量给定脸部的质量，并且引入了自适应机制以学习一个良好结构的内类特征分布，从而改进野外面部识别；在面部识别、质量评估和聚类的广泛实验中，表现优于现有技术。

MagFace：一种用于人脸识别和质量评估的通用表示方法