Progressive structure enhancement graph convolutional network for face clustering

Face clustering, a technique for automatically annotating large-scale face data, has made significant advancements with the advent of graph convolutional networks (GCNs). Despite their success, GCNs can suffer from decreased performance due to conflicting information passed along noisy edges of a graph. To address this issue, we propose a novel framework named progressive structure enhancement GCN (PSE-GCN), which combines graph structure learning with graph-guided feature aggregation. Our PSE-GCN framework includes a dynamic graph construction (DGC) module that enhances local relationships and suppresses global noise, thereby improving the quality of the graph. By stacking multiple DGCs, PSE-GCN progressively refines the graph quality and yields discriminative features for various clustering tasks. Additionally, we introduce a subgraph-based neighborhood re-ranking (SNR) mechanism that improves graph homogeneity by rearranging the candidate neighbors of each face based on structural similarity at the subgraph level. Our experimental results, conducted on several popular benchmarks, not only demonstrate the effectiveness of PSE-GCN, but also show that it outperforms state-of-the-art methods, e. g. , 93. 50% in pairwise F-score on the MS-Celeb-1M dataset.

Authors

• Shaoying Li
• Wei Yao
• Yuan Gao Northwestern Polytechnical University
• Yinchi Ma
• Bo Liu Peking University

Keywords

• Face clustering
• Graph convolutional network
• Graph structure learning