Kriegman-Belhumeur Vision Technologies

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Kriegman-Belhumeur Vision Technologies

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by Peter N. Belhumeur , David W. Jacobs , David J. Kriegman , Neeraj Kumar

BibTeX

@MISC{Belhumeur_kriegman-belhumeurvision,
    author = {Peter N. Belhumeur and David W. Jacobs and David J. Kriegman and Neeraj Kumar},
    title = {Kriegman-Belhumeur Vision Technologies},
    year = {}
}

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Abstract

We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a non-parametric set of global models for the part locations based on over one thousand hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting and occlusion than prior ones. We show excellent performance on a new dataset gathered from the internet and show that our localizer achieves state-of-theart performance on the less challenging BioID dataset. 1.

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