Framework for Active Clustering With Ensembles

Clustering approaches can alleviate the burden of tagging face identities in ad hoc video and image collections. We introduce a novel semisupervised framework for clustering face patterns into identity groups using minimal human interaction. This technique combines concepts from ensemble clustering and active learning to improve clustering accuracy. The framework actively queries the user for a soft link constraint between each pair of neighboring faces that are ambiguously matched according to the ensemble. We demonstrate the efficacy of our approach with the broadest evaluation of active face clustering algorithms to date. Our evaluations focus on data that is appropriate for human-in-the-loop face recognition, including blurry point-and-shoot videos, images of women seen before and after the application of makeup, and photographs of twins. The results indicate that ensemble-based constrained clustering algorithms are generally more robust to noise than alternative approaches. Finally, we show that the proposed clustering algorithm is more accurate and parsimonious than the current state-of-the-art.