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Probabilistic Methods for Face Registration and Recognition

Probabilistic Methods for Face Registration and Recognition

Peng Li, Peng Li, Simon J. D. Prince, Simon J. D. Prince
Copyright: © 2011 |Pages: 20
ISBN13: 9781615209910|ISBN10: 1615209913|EISBN13: 9781615209927
DOI: 10.4018/978-1-61520-991-0.ch010
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MLA

Li, Peng, et al. "Probabilistic Methods for Face Registration and Recognition." Advances in Face Image Analysis: Techniques and Technologies, edited by Yu-Jin Zhang, IGI Global, 2011, pp. 178-197. https://doi.org/10.4018/978-1-61520-991-0.ch010

APA

Li, P., Li, P., Prince, S. J., & Prince, S. J. (2011). Probabilistic Methods for Face Registration and Recognition. In Y. Zhang (Ed.), Advances in Face Image Analysis: Techniques and Technologies (pp. 178-197). IGI Global. https://doi.org/10.4018/978-1-61520-991-0.ch010

Chicago

Li, Peng, et al. "Probabilistic Methods for Face Registration and Recognition." In Advances in Face Image Analysis: Techniques and Technologies, edited by Yu-Jin Zhang, 178-197. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61520-991-0.ch010

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Abstract

In this chapter the authors review probabilistic approaches to face recognition and present extended treatment of one particular approach. Here, the face image is decomposed into an additive sum of two parts: a deterministic component, which depends on an underlying representation of identity and a stochastic component which explains the fact that two face images from the same person are not identical. Inferences about matching are made by comparing different probabilistic models rather than comparing distance to an identity template in some projected space. The authors demonstrate that this model comparison is superior to distance comparison. Furthermore, the authors show that performance can be further improved by sampling the feature space and combining models trained using these feature subspaces. Both random sampling with and without replacement significantly improves performance. Finally, the authors illustrate how this probabilistic approach can be adapted for keypoint localization (e.g. finding the eyes, nose and mouth etc.). The keypoints can either be (1) explicitly localized by evaluating the likelihood of all the possible locations in the given image, or (2) implicitly localized by marginalizing over possible positions in a Bayesian manner. The authors show that recognition and keypoint localization performance are comparable to using manual labelling.

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