Face clustering tasks can learn hierarchical semantic information from large-scale data, which has the potential to help facilitate face recognition. However, there are few works on this problem. This paper explores it by proposing a joint optimization task of label classification and supervised contrastive clustering to introduce the cluster knowledge to the traditional face recognition task in two ways. We first extend ArcFace with a cluster-guided angular margin to adjust the within-class feature distribution according to the hard level of face clustering. Secondly, we propose a supervised contrastive clustering approach to pull the features to the cluster center and propose the cluster-aligning procedure to align the cluster center and the learnable class center in the classifier for joint training. Finally, extensive qualitative and quantitative experiments on popular facial benchmarks demonstrate the effectiveness of our paradigm and its superiority over the existing approaches to face recognition.

翻译：人脸聚类任务能够从大规模数据中学习层次化语义信息，这对促进人脸识别具有潜在价值。然而，该问题的相关研究仍较为匮乏。本文通过提出标签分类与监督对比聚类的联合优化任务，从两个维度将聚类知识引入传统人脸识别任务。首先，我们扩展了ArcFace，引入聚类引导的角间距机制，根据人脸聚类的难易程度调整类内特征分布。其次，我们提出监督对比聚类方法，将特征拉向聚类中心，并设计聚类对齐流程，使聚类中心与分类器中可学习的类别中心实现联合训练对齐。最后，在主流人脸基准数据集上进行的定性与定量实验，充分验证了本方法的有效性及其相较于现有人脸识别方法的优越性。