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ICCV
2011
IEEE

Learning Universal Multi-view Age Estimator by Video Contexts

8 years 11 months ago
Learning Universal Multi-view 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 i...
Zheng Song, Bingbing Ni, Dong Guo, Terence Sim, Sh
Added 11 Dec 2011
Updated 11 Dec 2011
Type Journal
Year 2011
Where ICCV
Authors Zheng Song, Bingbing Ni, Dong Guo, Terence Sim, Shuicheng Yan
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