Learning patch correspondences for improved viewpoint invariant face recognition

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.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Ahmed Bilal Ashraf, Simon Lucey, Tsuhan Chen
• Keywords: Computer Vision, CVPR 2008, Face Change, Face Patch Varies, Face Recognition Problem, Viewpoint Invariant Face, Viewpoint Varies