NDLI: Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference

Content Provider	IEEE Xplore Digital Library
Author	Yi Zhou Lie Gu Hong-Jiang Zhang
Copyright Year	2003
Description	Author affiliation: Peking Univ., Beijing, China (Yi Zhou)
Abstract	In this paper we study the problem of shape analysis and its application in locating facial feature points on frontal faces. We propose a Bayesian inference solution based on tangent shape approximation called Bayesian tangent shape model (BTSM). Similarity transform coefficients and the shape parameters in BTSM are determined through MAP estimation. Tangent shape vector is treated as the hidden state of the model, and accordingly, an EM based searching algorithm is proposed to implement the MAP procedure. The major results of our algorithm are: 1) tangent shape is updated by a weighted average of two shape vectors, the projection of the observed shape onto tangent space, and the reconstruction of shape parameters. 2) Shape parameters are regularized by multiplying a ratio of the noise variations, which is a continuous function instead of a truncated function. We discussed the advantages conveyed by these results, and demonstrate the accuracy and the stability of the algorithm by extensive experiments.
Sponsorship	IEEE Comput. Soc Tech. Committee on Pattern Anal. & Machine Intelligence (TCPAMI)
File Size	1699698
File Format	PDF
ISBN	0769519008
ISSN	10636919
DOI	10.1109/CVPR.2003.1211344
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-06-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Shape Asia Deformable models Facial features Inference algorithms Image reconstruction Multi-stage noise shaping Signal to noise ratio Stability
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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