Density Division Face Clustering Based on Graph Convolutional Networks

Qingchao Zhao, Long Li, Yan Chu, Zhengkui Wang, Wen Shan

Abstract: Supervised clustering methods cluster images using graph convolutional networks (GCN) via linkage prediction, and have shown significant improvements over the traditional clustering algorithms (e.g., K-means, DBScan, etc.) in terms of clustering effectiveness. However, existing supervised clustering approaches are always time-consuming, which may limit their usage. The high computation overhead is mainly resulted from generating and processing a large amount of subgraphs, each of which is generated for one image instance in order to infer the linkage between them. To tackle the high computation problem, we propose a new density division clustering approach based on GCN, and our experiments demonstrate that the new approach is both time-efficient and effective. The approach divides the data into high-density and low-density parts, and only performs GCN subgraph link inference on the low-density parts, which highly reduces redundant calculations. Meanwhile, to ensure sufficient contextual information extraction for low-density parts, it generates adaptive subgraphs instead of fixed-size subgraphs. Our experimental evaluations over multiple datasets show that our proposed approach is five-time faster than state-of-the-art algorithms with even higher accuracy.