Abstract
Face recognition sees remarkable progress in recent
years, and its performance has reached a very high level.
Taking it to a next level requires substantially larger data,
which would involve prohibitive annotation cost. Hence,
exploiting unlabeled data becomes an appealing alternative. Recent works have shown that clustering unlabeled
faces is a promising approach, often leading to notable performance gains. Yet, how to effectively cluster, especially
on a large-scale (i.e. million-level or above) dataset, remains an open question. A key challenge lies in the complex variations of cluster patterns, which make it difficult for
conventional clustering methods to meet the needed accuracy. This work explores a novel approach, namely, learning to cluster instead of relying on hand-crafted criteria.
Specifically, we propose a framework based on graph convolutional network, which combines a detection and a segmentation module to pinpoint face clusters. Experiments
show that our method yields significantly more accurate
face clusters, which, as a result, also lead to further performance gain in face recognition.