Issue No. 12 - Dec. (2013 vol. 35)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.23
Peter N. Belhumeur , Dept. of Comput. Sci., Columbia Univ., New York, NY, USA
David W. Jacobs , Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA
David J. Kriegman , Dept. of Comput. Sci. & Eng., Univ. of California, San Diego, La Jolla, CA, USA
Neeraj Kumar , Univ. of Washington, Seattle, WA, USA
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.
Detectors, Facial features, Feature extraction, Biometrics, Shape analysis
P. N. Belhumeur, D. W. Jacobs, D. J. Kriegman and N. Kumar, "Localizing Parts of Faces Using a Consensus of Exemplars," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 12, pp. 2930-2940, 2013.