Temporal face embedding and propagation in photo collections

We present a two-step approach for modeling facial variations and class likelihoods over time. Unlike traditional approaches, we explicitly model the temporal domain that is often available, for example, in consumer photos or surveillance systems. Our combined approach draws upon the concepts of manifold transformation and semi-supervised graph-based propagation to simultaneously recognize faces across entire photo collections. Experiments on two datasets demonstrate improved face recognition accuracy.