Learning Universal Multiview Age Estimator by Video Contexts

Most existing techniques for analyzing face images assume that the faces are at near-frontal poses. Generalizing to non-frontal faces is often difficult, due to a dearth of ground truths for non-frontal faces and also the inherent challenges of handling pose variations. In this work, we investigate how to learn universal multi-view age estimator by harnessing 1) the rich video contexts, 2) publicly available labeled frontal face corpus, and 3) a limited number of, even zero in theory, non-frontal faces with age labels. First, a diverse human-involved video corpus with about 9, 000 clips is collected from online video sharing website such as YouTube.com. Then, multi-view face detection and tracking are performed to build a large set of frontal-vs-profile face bundles, ∼20, 000, each of which is from the same tracking sequence, and thus naturally with identical age. These unlabeled face bundles constitute the so-called video contexts, and the parametric multi-view age estimator is inferred by 1) enforcing the face-to-age relation for the partially labeled faces, 2) imposing the consistency of the predicted ages for the non-frontal and frontal faces within each face bundle, and 3) mutually constraining the multi-view age models with the spatial correspondence priors derived from the face bundles. The derived multi-view age estimator shows promising performance on a collected evaluation dataset with faces in different views from the Internet, whose age information is annotated manually with guidance from their surrounding texts .