Locally Linear Montage
Face recognition under varying illumination remains a challenging
problem. Much progress has been made toward a solution through methods
that require multiple gallery images of each subject under varying
illumination. Yet for many applications, this requirement is too
severe. We propose a novel method that requires only a single gallery
image per subject taken under unknown lighting. The method builds
upon two contributions. We first estimate the lighting from its
reflection in the eyes. This allows us to explicitly recover the
illumination in the single gallery images as well as the probe image.
Next, we exploit the local linearity of face appearance variation
across different people. We represent the gallery images as locally
linear montages of images of many different faces taken under the
same lighting (bootstrap images). Then, we transfer the estimated
combination of bootstrap images to synthesize each subject's face
under the probe lighting to accomplish recognition. We show through
tests on the CMU PIE database that we can achieve better recognition
results using our lighting estimation method and locally linear
montages than the current state-of-the-art. (with P.N.
Belhumeur and S.K.
Nayar)
Primary Reference
(TBA)
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