This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many paper...This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many papers about the fitting method of AAM,this paper treats how images are chosen for fitting of the rotated face in modelling process.To solve this problem,databases of facial rotation and expression are selected and models are built using Procrustes method and principal component analysis(PCA).These models are applied in fitting methods like basic AAM fitting,inverse compositional alignment(ICA),project-out ICA,normalization ICA,robust normalization inverse compositional algorithm(RNIC)and efficient robust normalization algorithm(ERN).RNIC and ERN can fit the rotated face in images efficiently.The efficiency of model building is checked using sequence images made by ourselves.展开更多
Active appearance model（AAM） is an efficient useful for the subsequent work such as face detection and method for the localization of facial feature points, which is also facial expression recognition. In this paper...Active appearance model（AAM） is an efficient useful for the subsequent work such as face detection and method for the localization of facial feature points, which is also facial expression recognition. In this paper, we mainly discuss the AAMs based on principal component analysis （PCA）. We also propose an efficient facial fitting algorithm, which is named inverse compositional image alignment （ICIA）, to eliminate a considerable amount of computation resulting from traditional gradient descent fitting algorithm. Finally, 3D facial curvature is used to initialize the location of facial feature, which helps select the parameters of initial state for the improved AAM.展开更多
基金Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(No.2012M3C4A7032182)The MSIP(Ministry of Science,ICT&Future Planning),Korea,under the ITRC(Information Technology Research Center)support program(NIPA-2013-H0301-13-2006)supervised by the NIPA(National IT Industry Promotion Agency)
文摘This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many papers about the fitting method of AAM,this paper treats how images are chosen for fitting of the rotated face in modelling process.To solve this problem,databases of facial rotation and expression are selected and models are built using Procrustes method and principal component analysis(PCA).These models are applied in fitting methods like basic AAM fitting,inverse compositional alignment(ICA),project-out ICA,normalization ICA,robust normalization inverse compositional algorithm(RNIC)and efficient robust normalization algorithm(ERN).RNIC and ERN can fit the rotated face in images efficiently.The efficiency of model building is checked using sequence images made by ourselves.
基金The MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency)(NIPA-2012-H0301-12-2006)TheBrain Korea 21 Project in 2012
文摘Active appearance model（AAM） is an efficient useful for the subsequent work such as face detection and method for the localization of facial feature points, which is also facial expression recognition. In this paper, we mainly discuss the AAMs based on principal component analysis （PCA）. We also propose an efficient facial fitting algorithm, which is named inverse compositional image alignment （ICIA）, to eliminate a considerable amount of computation resulting from traditional gradient descent fitting algorithm. Finally, 3D facial curvature is used to initialize the location of facial feature, which helps select the parameters of initial state for the improved AAM.
