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Cited by 518

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Localizing parts of faces using a consensus of exemplars

Belhumeur, Peter N., Jacobs, David W., Kriegman, David J., Kumar, Neeraj·2011·CVPR 2011

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-the-art performance on the less challenging BioID dataset.

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Metadata

Authors
Belhumeur, P. N., Jacobs, D. W., Kriegman, D. J., & Kumar, N.
Venue
CVPR 2011
Publisher
IEEE
Published
2011-06
Pages
545-552
OpenAlex
W2032558548
Cited by
518
References
1
FWCI
43.52
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Localizing parts of faces using a consensus of exemplars. https://app.cochat.ai/papers/10.1109/cvpr.2011.5995602
The paper
Belhumeur, P. N., Jacobs, D. W., Kriegman, D. J., & Kumar, N. (2011). Localizing parts of faces using a consensus of exemplars. CVPR 2011. https://doi.org/10.1109/cvpr.2011.5995602

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.