Incremental face clustering with optimal summary learning via graph convolutional network
Xuan Zhao, Zhongdao Wang, Lei Gao, Yali Li, Shengjin Wang
Abstract
In this study, we address the problems encountered by incremental face clustering. Without the benefit of having observed the entire data distribution, incremental face clustering is more challenging than static dataset clustering. Conventional methods rely on the statistical information of previous clusters to improve the efficiency of incremental clustering; thus, error accumulation may occur. Therefore, this study proposes to predict the summaries of previous data directly from data distribution via supervised learning. Moreover, an efficient framework to cluster previous summaries with new data is explored. Although learning summaries from original data costs more than those from previous clusters, the entire framework consumes just a little bit more time because clustering current data and generating summaries for new data share most of the calculations. Experiments show that the proposed approach significantly outperforms the existing incremental face clustering methods, as evidenced by the improvement of average F-score from 0.644 to 0.762. Compared with state-of-the-art static face clustering methods, our method can yield comparable accuracy while consuming much less time.