Abstract In this paper, we propose a two-level integrated model for accurate face shape alignment. At the low level, the shape is split into a set of line segments which serve as the nodes in the hidden layer of a Markov Network. At the high level, all the line segments are constrained by a global Gaussian point distribution model. Furthermore, those already accurately aligned points from the low level are detected and constrained using a constrained regularization algorithm. By analyzing the regularization result, a mask image of local minima is generated to guide the distribution of Markov Network states, which makes our algorithm more robust. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.
An Integrated Model for Accurate Shape Alignment
Computer Vision – ECCV 2006 ; 6 ; 333-346
Lecture Notes in Computer Science ; 3954 , 6
2006-01-01
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
An Integrated Model for Accurate Shape Alignment
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