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| Human Face Analysis with Nonlinear Manifold Learning |
| Wang Xiao-kan① Mao Xia① Ishizuka Mitsuru② |
①(School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China)
②(Department of Information and Communication Engineering, University of Tokyo, Japan) |
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Abstract Since human face movements distribute on a nonlinear manifold, there are inherent alignment residuals brought by the global linearity hypothesis in the traditional Principal Component Analysis (PCA) based Active Appearance Models (AAM). In this paper, a famous manifold learning method, Local Linear Embedding (LLE) is improved to model human face shape space for reducing the inherent alignment residuals. The experimental results show that the method, LLE-AAM, obtains lower alignment residuals to the tiny alterations of human face and still make successful alignment when PCA-AAM failed to some large alterations. According to the statistical analysis, LLE-AAM could reduce the residual to a certain extent.
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Received: 28 February 2011
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Corresponding Authors:
Mao Xia
E-mail: moukyoucn@yahoo.com.cn
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2011, Vol. 33 
