Paper Info
Title
Facial Shape Localization Using Probability Gradient Hints
Abstract
This letter proposes a novel method to localize facial shape represented by a series of facial landmarks. In our method, the problem of facial shape localization is formulated with a Bayesian inference. Specifically, given a face image, the posterior probability of the facial shape is naturally decomposed into two parts: the likelihood function of local textures and the prior constraints of global shape. The former is provided by the landmark detectors, while the latter is evaluated based on the global shape statistics. The global shape is iteratively estimated in the Maximum A Posteriori (MAP) procedure which is derived in a Lucas-Kanade manner over the probability distribution. Intuitively, in each step, the landmarks are driven by the probability gradient and converge towards the positions which maximize the posterior probability. Experiments on two public databases (XM2VTS and BioID) show the effectiveness of the proposed method.
Year
DOI
Venue
2009
10.1109/LSP.2009.2026457
IEEE Signal Process. Lett.
Keywords
Field
DocType
likelihood function,facial shape localization,belief networks,probability gradient hints,face recognition,bayesian inference,maximum a posteriori estimation,maximum likelihood estimation,posterior probability,facial landmarks,global shape,boosting,probability,face detection,statistical distributions,bayesian methods,probability distribution,lucas kanade,active shape model,detectors,active appearance model
Active shape model,Facial recognition system,Likelihood function,Bayesian inference,Pattern recognition,Posterior probability,Active appearance model,Probability distribution,Artificial intelligence,Maximum a posteriori estimation,Mathematics
Journal
Volume
Issue
ISSN
16
10
1070-9908
Citations 
PageRank 
References 
4
0.42
16
Authors
3
Name
Order
Citations
PageRank
Zhiheng Niu1824.46
Shiguang Shan26322283.75
Xilin Chen36291306.27