Efficient model building in active appearance model for rotated face

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.

Facial model fitting algorithm based on active appearance model

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.

Tracking facial features with occlusions

Facial expression recognition consists of determining what kind of emotional content is presented in a human face. The problem presents a complex area for exploration, since it encompasses face acquisition, facial feature tracking, facial ex- pression classification. Facial feature tracking is of the most interest. Active Appearance Model (AAM) enables accurate tracking of facial features in real-time, but lacks occlusions and self-occlusions. In this paper we propose a solution to improve the accuracy of fitting technique. The idea is to include occluded images into AAM training data. We demonstrate the results by running ex- periments using gradient descent algorithm for fitting the AAM. Our experiments show that using fitting algorithm with occluded training data improves the fitting quality of the algorithm.

Coherent Point Drift Registration Combined with Image Feature and its Application

A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences.In this paper,we introduce a novel point correspondence method(FB-CPD),which can improve the accuracy of coherent point drift(CPD) by using the information of image feature.The objective function of the proposed method is defined by both of geometric spatial information and image feature information,and the origin Gaussian mixture model in CPD is modified according to the image feature of points.FB-CPD is tested on the 3D prostate and liver point sets through the simulation experiments.The registration error can be reduced efficiently by FB-CPD.Moreover,the active appearance model constructed by FB-CPD can obtain fine segmentation in 3D CT prostate image.Compared with the original CPD,the overlap ratio of voxels was improved from 88.7% to 90.2% by FB-CPD.