期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 35, 期 12, 页码 2930-2940出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2013.23
关键词
Part localization; faces; biometrics; fiducial points
资金
- US Government
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.
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