Learn to Cluster Faces via Pairwise Classification
This work addresses memory and efficiency issues in face clustering for real-world applications, representing an incremental improvement over existing graph-based methods.
The paper tackles the problem of excessive memory consumption and reliance on empirical thresholds in graph-based face clustering by formulating it as a pairwise relationship classification task, achieving state-of-the-art performance on public benchmarks with the fastest speed and reduced memory usage.
Face clustering plays an essential role in exploiting massive unlabeled face data. Recently, graph-based face clustering methods are getting popular for their satisfying performances. However, they usually suffer from excessive memory consumption especially on large-scale graphs, and rely on empirical thresholds to determine the connectivities between samples in inference, which restricts their applications in various real-world scenes. To address such problems, in this paper, we explore face clustering from the pairwise angle. Specifically, we formulate the face clustering task as a pairwise relationship classification task, avoiding the memory-consuming learning on large-scale graphs. The classifier can directly determine the relationship between samples and is enhanced by taking advantage of the contextual information. Moreover, to further facilitate the efficiency of our method, we propose a rank-weighted density to guide the selection of pairs sent to the classifier. Experimental results demonstrate that our method achieves state-of-the-art performances on several public clustering benchmarks at the fastest speed and shows a great advantage in comparison with graph-based clustering methods on memory consumption.