①(北京航空航天大学电子信息工程学院 北京 100191) ②(Department of Information and Communication Engineering, University of Tokyo, Japan)
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)
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.
2011, Vol. 33 
