Lab Meeting November 3rd, 2008 (swem): Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition

Title: Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition

Author: Ahmed Bilal Ashraf, Simon Lucey, Tsuhan Chen

Abstract:
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.

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