title: Localizing parts of faces using a consensus of exemplars.
abstract: 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.
authors.affiliation: Columbia University, New York.
authors.forename: Peter N
authors.initials: PN
authors.lastname: Belhumeur
authors.forename: David W
authors.initials: DW
authors.lastname: Jacobs
authors.forename: David J
authors.initials: DJ
authors.lastname: Kriegman
authors.forename: Neeraj
authors.initials: N
authors.lastname: Kumar
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D032921:Consensus; D005145:Face; D006801:Humans; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual