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|Title:||Learning universal multi-view age estimator using video context|
|Source:||Song, Z.,Ni, B.,Guo, D.,Sim, T.,Yan, S. (2011). Learning universal multi-view age estimator using video context. Proceedings of the IEEE International Conference on Computer Vision : 241-248. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2011.6126248|
|Abstract:||Many existing techniques for analyzing face images assume that the faces are at nearly frontal. Generalizing to non-frontal faces is often difficult, due to a dearth of ground truth for non-frontal faces and also to the inherent challenges in handling pose variations. In this work, we investigate how to learn a universal multi-view age estimator by harnessing 1) unlabeled web videos, 2) a publicly available labeled frontal face corpus, and 3) zero or more non-frontal faces with age labels. First, a large diverse human-involved video corpus is collected from online video sharing website. Then, multi-view face detection and tracking are performed to build a large set of frontal-vs-profile face bundles, each of which is from the same tracking sequence, and thus exhibiting the same age. These unlabeled face bundles constitute the so-called video context, and the parametric multi-view age estimator is trained 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. Our multi-view age estimator performs well on a realistic evaluation dataset that contains faces under varying poses, and whose ground truth age was manually annotated. © 2011 IEEE.|
|Source Title:||Proceedings of the IEEE International Conference on Computer Vision|
|Appears in Collections:||Staff Publications|
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