Improved Face Representation via Joint Label Classification and
  Supervised Contrastive Clustering
Zhenduo Zhang
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.