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Learning patch correspondences for improved viewpoint invariant face recognition

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3 Author(s)
Ashraf, A.B. ; Carnegie Mellon Univ., Pittsburgh, PA ; Lucey, S. ; Tsuhan Chen

Variation due to viewpoint is one of the key challenges that stand in the way of a complete solution to the face recognition problem. It is easy to note that local regions of the face change differently in appearance as the viewpoint varies. Recently, patch-based approaches, such as those of Kanade and Yamada, have taken advantage of this effect resulting in improved viewpoint invariant face recognition. In this paper we propose a data-driven extension to their approach, in which we not only model how a face patch varies in appearance, but also how it deforms spatially as the viewpoint varies. We propose a novel alignment strategy which we refer to as ldquostack flowrdquo that discovers viewpoint induced spatial deformities undergone by a face at the patch level. One can then view the spatial deformation of a patch as the correspondence of that patch between two viewpoints. We present improved identification and verification results to demonstrate the utility of our technique.

Published in:

Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on

Date of Conference:

23-28 June 2008

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