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Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition
A.B. Ashraf, S. Lucey, and T. Chen
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.

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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 “stack flow” 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.

Associated center: VASC
Associated labs/groups: Human Identification at a Distance and Face Group
Associated project: Face Recognition Across Pose

A.B. Ashraf, S. Lucey, and T. Chen, "Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.

@inproceedings{Ashraf_2008_6027,
   author = "Ahmed Bilal Ashraf and Simon Lucey and Tsuhan Chen",
   title = "Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition",
   booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2008"
}