Title :
Direct appearance models
Author :
Hou, Xin Wen ; Li, Stan Z. ; Zhang, Hongjiang ; Cheng, Qiansheng
Author_Institution :
Inst. of Math. Sci., Peking Univ., Beijing, China
Abstract :
Active appearance model (AAM), which makes ingenious use of both shape and texture constraints, is a powerful tool for face modeling, alignment and facial feature extraction under shape deformations and texture variations. However, as we show through our analysis and experiments, there exist admissible appearances that are not modeled by AAM and hence cannot be reached by AAM search; also the mapping from the texture subspace to the shape subspace is many-to-one and therefore a shape should be determined entirely by the texture in it. We propose a new appearance model, called direct appearance model (DAM), without combining from shape and texture as in AAM. The DAM model uses texture information directly in the prediction of the shape and in the estimation of position and appearance (hence the name DAM). In addition, DAM predicts the new face position and appearance based on principal components of texture difference vectors, instead of the raw vectors themselves as in AAM. These lead to the following advantages over AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) convergence and accuracy are improved, and (3) memory requirement is cut down to a large extent. The advantages are substantiated by comparative experimental results.
Keywords :
face recognition; image texture; principal component analysis; AAM search; DAM model; DAM subspaces; active appearance model; admissible appearances; direct appearance models; face appearance; face modeling; face position; facial feature extraction; memory requirement; principal components; shape deformations; shape prediction; shape subspace; texture constraints; texture difference vectors; texture information; texture subspace; texture variations; Active appearance model; Active shape model; Deformable models; Face recognition; Facial features; Mathematical model; Pattern analysis; Power system modeling; Predictive models; Principal component analysis;
Conference_Titel :
Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on
Print_ISBN :
0-7695-1272-0
DOI :
10.1109/CVPR.2001.990568