On mitigating hard clusters for face clustering

• Main Authors: CHEN, Yingjie, ZHONG, Huasong, CHEN, Chong, SHEN, Chen, HUANG, Jianqiang, WANG, Tao, LIANG, Yun, Qianru SUN
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: face clustering; unsupervised learning; density estimation; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/7512; https://ink.library.smu.edu.sg/context/sis_research/article/8515/viewcontent/ECCV2022_FaceClustering.pdf
• Institution: Singapore Management University

Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images. It remains challenging to identify small or sparse face image clusters that we call hard clusters, which is caused by the heterogeneity, i.e., high variations in size and sparsity, of the...