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	Texture constrained facial feature point tracking
	Song Gang ; Ai Hai-Zhou ; Xu Guang-You
	2010-05-06 ; 2010-05-06
关键词	Practical Theoretical or Mathematical/ Bayes methods correlation methods face recognition feature extraction image texture optical tracking/ texture constrained facial feature point tracking Lucas-Kanade optical flow tracking algorithm face alignment statistical model direct appearance model Bayesian framework feature position prediction inter-frame correlations face movements face recognition 3D face modeling feature point localization Bayesian posterior probability estimation/ B6135E Image recognition B0240Z Other topics in statistics C5260B Computer vision and image processing techniques C1140Z Other topics in statistics C1250M Image recognition
中文摘要	In this paper, a facial feature point tracking scheme is proposed by integrating Lucas-Kanade optical flow tracking algorithm and the face alignment statistical model, DAM (direct appearance model), together in a Bayesian framework. The prediction of feature positions from Lucas-Kanade algorithm exploits the inter-frame correlations and accelerates the tracking speed. The texture-shape constraint under DAM improves the localization accuracy and robustness. Experiments show that this method adapts well to the various face movements. It can be used in face recognition or 3D face modeling.
语种	中文 ; 中文
出版者	Science Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/11189]
专题	清华大学
推荐引用方式 GB/T 7714	Song Gang,Ai Hai-Zhou,Xu Guang-You. Texture constrained facial feature point tracking[J],2010, 2010.
APA	Song Gang,Ai Hai-Zhou,&Xu Guang-You.(2010).Texture constrained facial feature point tracking..
MLA	Song Gang,et al."Texture constrained facial feature point tracking".(2010).

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