【摘要】 人脸聚类是人脸标注、人脸识别等工作的预处理过程。其主要对人脸图像进行分组，用来为人脸识别模型提供高质量的标注信息，从而有效降低人工标注的成本。人脸聚类的关键在于如何学习大规模人脸数据中整体及局部的结构关系，并把其迁移至待标注数据集。针对这一问题，本文提出一种基于近邻关系聚合的人脸聚类方法(Nearest Neighborhood Aggregation Clustering, NNAC)。该方法把局部结构的学习建模为一个近邻关系预测问题，通过堆叠多个改进的基于残差-全连接模块(Residual Fully-Connected Block, ResFCB)以提取多尺度的邻接关系特征。实验结果表明，相比主流人脸聚类方法，该方法在基准数据集上能够有效提升聚类精度。更多还原

【Abstract】 Face clustering is a pre-processing process for face annotation, face recognition and other tasks. It can reduce the labelling burden and provide high-quality annotation for face recognition models by grouping face images. The challenge of face clustering is to extract the global and local structural knowledge in large-scale face datasets and transfer it to the unlabelled ones. To address the issue, a face clustering method based on nearest neighbor aggregation is proposed. The method formulates local structure learning as a link prediction problem. It extracts multi-scale neighborhood characteristics by multiple improved residual Fully-Connected block. The experimental results show that the proposed method can effectively improve the clustering accuracy on the benchmark compared with the mainstream face clustering methods.更多还原