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We study the problem of dense wide baseline stereo with varying illumination. We
are motivated by the problem of face recognition across pose. Stereo matching
allows us to compare face images based on physically valid, dense
correspondences. We show that the stereo matching cost provides a very robust
measure of the similarity of faces that is insensitive to pose variations. We
build on the observation that most illumination insensitive local comparisons
require the use of relatively large windows. The size of these windows is
affected by foreshortening. If we do not account for this effect, we incur
misalignments that are systematic and significant and are exacerbated by wide
baseline conditions.
We present a general formulation of dense wide baseline stereo with varying
illumination and provide two methods to solve them. The first method is based on
dynamic programming (DP) and fully accounts for the effect of slant. The second
method is based on graph cuts (GC) and fully accounts for the effect of both slant
and tilt. The GC method finds a global solution using the unary function from
the general formulation and a novel smoothness term that encodes surface
orientation.
Our experiments show that DP dense wide baseline stereo achieves superior
performance compared to existing methods in face recognition across pose. The
experiments with the GC method show that accounting for both slant and tilt can
improve performance in situations with wide baselines and lighting variation.
Our formulation can be applied to other more sophisticated window based image
comparison methods for stereo
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