Person-specific face recognition in unconstrained environments: a combination of offline and online learning

This paper studies face recognition and person-specific face image retrieval in unconstrained environments. The proposed method consists of two parts: offline and online learning. In offline stage, we take advantage of both global and local features in a Bayesian framework for generic face recognition. In online stage, the offline learned classifier is adapted according to the query images of a given person, from which a person-specific face image retriever can be obtained. Our method is applied to the ldquolabeled faces in the wildrdquo dataset, which is more realistic than usual face recognition datasets. We show that the combination of offline and online learning can yield very promising results.