FC-DGCN: Deep Graph Convolutional Network for Face Clustering and Recognition

Face recognition has achieved strong performance, but further gains often require much larger labeled datasets, making annotation costly. This motivates the use of unlabeled data, where face clustering is important for applications such as annotation and recognition. Recent GCN-based methods have shown promising results on affinity graphs, but balancing clustering quality and scalability on large-scale data remains challenging. To solve this problem, we propose FC-DGCN, a face clustering framework based on deep residual GCNs. The proposed method formulates clustering on an affinity graph through two complementary tasks: node confidence estimation and edge connectivity estimation. Specifically, we develop DGCN-V to estimate node confidence on the global graph and DGCN-E to estimate edge connectivity on local subgraphs. To better characterize the local cluster structure, we further revise the confidence-estimation target. Based on these two modules, FC-DGCN links each node to a higher-confidence neighbor with strong predicted connectivity, which naturally induces identity-consistent clusters. Experiments on MS-Celeb-1M show that FC-DGCN outperforms competitive baselines in Pairwise F-score and BCubed F-score while remaining computationally practical. Moreover, the clusters generated by FC-DGCN provide effective pseudo labels for low-label face recognition, leading to substantial improvements on MegaFace and IJB-A.