Face Clustering via Graph Convolutional Networks with Confidence Edges

Abstract

Face clustering is a method for unlabeled image annotation and has attracted increasing attention. Existing methods have made significant breakthroughs by introducing Graph Convolutional Networks (GCNs) on the affinity graph. However, such graphs will contain many vertex pairs with inconsistent similarities and labels, thus degrading the model's performance. There are already relevant efforts for this problem, but the information about features needs to be mined further. In this paper, we define a new concept called confidence edge and guide the construction of graphs. Furthermore, a novel confidence-GCN is proposed to cluster face images by deriving more confidence edges. Firstly, Local Information Fusion is advanced to obtain a more accurate similarity metric by considering the neighbors of vertices. Then Unsupervised Neighbor Determination is used to discard low-quality edges based on similarity differences. Moreover, we elaborate that the remaining edges retain the most beneficial information to demonstrate the validity. At last, the confidence-GCN takes the graph as the input and fully uses the confidence edges to complete the clustering. Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art. At the same time, comparable results are obtained on the fashion dataset.