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.

翻译：人脸聚类可以为海量未标注的人脸数据提供伪标签，从而提升不同人脸识别模型的性能。现有的聚类方法通常基于统一阈值或学习到的截止位置在子图内聚合特征，这可能会降低子图的召回率，进而影响聚类性能。本文提出了一种高效的邻域感知子图调整方法，能够显著降低噪声并提高子图的召回率，从而驱动远距离节点向同一中心收敛。具体而言，该方法包含两个组成部分：利用邻居嵌入进行人脸嵌入增强，以及为提取结构信息而构建节点对的封闭子图。通过组合这些嵌入来预测所有节点对的连接概率，以替代余弦相似度，从而生成可用于GCN或其他聚类方法进一步聚合的新子图。通过在三个基准数据集上针对一系列聚类解决方案进行大量实验验证，数值结果表明该方法在泛化能力上优于现有最先进技术。