Learn to Cluster Faces with Better Subgraphs

• URL: http://arxiv.org/abs/2304.10831v1
• Date: Fri, 21 Apr 2023 09:18:55 GMT
• Title: Learn to Cluster Faces with Better Subgraphs
• Authors: Yuan Cao, Di Jiang, Guanqun Hou, Fan Deng, Xinjia Chen, Qiang Yang
• Abstract summary: Face clustering can provide pseudo-labels to the massive unlabeled face data. Existing clustering methods aggregate features within subgraphs based on a uniform threshold or a learned cutoff position. This work proposed an efficient neighborhood-aware subgraph adjustment method that can significantly reduce the noise.
• Score: 13.511058277653122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face clustering can provide pseudo-labels to the massive unlabeled face data and improve the performance of different face recognition models. The existing clustering methods generally aggregate the features within subgraphs that are often implemented based on a uniform threshold or a learned cutoff position. This may reduce the recall of subgraphs and hence degrade the clustering performance. This work proposed an efficient neighborhood-aware subgraph adjustment method that can significantly reduce the noise and improve the recall of the subgraphs, and hence can drive the distant nodes to converge towards the same centers. More specifically, the proposed method consists of two components, i.e. face embeddings enhancement using the embeddings from neighbors, and enclosed subgraph construction of node pairs for structural information extraction. The embeddings are combined to predict the linkage probabilities for all node pairs to replace the cosine similarities to produce new subgraphs that can be further used for aggregation of GCNs or other clustering methods. The proposed method is validated through extensive experiments against a range of clustering solutions using three benchmark datasets and numerical results confirm that it outperforms the SOTA solutions in terms of generalization capability.

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