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Localizing Parts of Faces Using a Consensus of Exemplars

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4 Author(s)
Belhumeur, P.N. ; Dept. of Comput. Sci., Columbia Univ., New York, NY, USA ; Jacobs, D.W. ; Kriegman, D.J. ; Kumar, N.

We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 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 real-world face datasets such as Labeled Faces in the Wild (LFW) and a new Labeled Face Parts in the Wild (LFPW) and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:35 ,  Issue: 12 )
Biometrics Compendium, IEEE

Date of Publication:

Dec. 2013

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