Research On 3D Face Recognition Under Expression Variations Based On Feature Extraction And Classification

Three-dimensional(3D)face recognition technology can make full use of spatial geometric information,and can get around the limitations of two-dimensional(2D)face recognition in light,makeup and gestures,so it has attracted extensive attention from more and more researchers.3D face is essentially a kind of non-rigid surfaces,whose expression variations can cause non-rigid deformations in the local region,especially for the lower half of the face,which contains the mouth area.This will affect the performance of 3D face recognition algorithms based on shape matching.Therefore,this paper analyzes the deformation of non-rigid free-form surface,and deeply studies the feature extraction and classification in 3D face recognition under expression variations.The main research results and innovative work are given as follows:(1)A method of feature-level fusion and feature-region fusion is proposed to extract the high discriminative features of 3D face.In order to reduce the adverse effect from the large facial expressions in the non-rigid regions as well as to maintain the topological integrity of 3D face surfaces,3D face is divided into semi-rigid region and non-rigid region,and the non-rigid point set registration is performed for non-rigid region using Coherent Point Drift(CPD)algorithm;then the feature-level fusion of low frequency and vertical high frequency features is carried out,and the semi-rigid region feature is combined with the non-rigid region feature after non-rigid point set registration for feature-region fusion.Feature-level fusion based on low-frequency and vertical high-frequency Haar wavelet feature is combined with the distinctive features to give full play to their strengths.The proposed feature-region fusion technology can effectively use the global information.(2)An adaptive feature selection method for expression-robust 3D face recognition is proposed.The proposed approach can more accurately use the difference of reconstruction residuals between the neutral and expression face.In order to avoid that all the non-rigid regions of the probe face are uniform treated,and to make full use of the geometry of the face,an irregular landmark-based patch facial representation based on the located facial landmarks is proposed;then,the multi-scale fusion of the pyramid local binary patterns(FPLBP)is proposed;finally,an adaptive feature selection method is proposed for the final classification and recognition based on reconstruction residual and accurately located landmarks.The proposed method can adaptively eliminate the facial area where the facial shape will distorts largely under expression variations to reduce the effect of the facial expression.(3)In order to avoid the complicated registration and threshold estimation,a 3D face recognition method is proposed based on local covariance descriptor directly extracted on the 3D face mesh and Riemannian kernel sparse representation-based classifier.Firstly,the keypoints are detected by the farthest point sampling method;secondly,different types of the efficient features are extracted to construct the local covariance descriptor with intrinsic property;finally,the similarity measure of the average sparse reconstruction residual is used to reduce intraclass differences while increasing interclass differences,and the appropriate Riemannian kernel sparse representation-based classifier is used for the final recognition.This method can quickly and effectively fuse different facial surface features to characterize the intrinsic property of the surface.(4)In order to make full use of the multi-scale information of 3D face as well as the locality of local covariance descriptor,a 3D face recognition method based on multi-scale local covariance descriptor and locality-sensitive Riemannian kernel sparse representation-based classifier is proposed.Firstly,the infinite feature selection method is used to select the feature with larger scores in the feature space to construct the local covariance descriptor,and the scale selection is based on the Root Mean Square Error(RMSE)of the eigenvalues of the covariance descriptors;secondly,the local covariance descriptors of the keypoints' neighborhood is extracted,and multi-scale fusion of these descriptors is performed;finally,3D face recognition is performed by locality-sensitive Riemannian kernel sparse representation-based classifier.In this method,the local covariance descriptor at different scales can be obtained by continuously changing the scale parameters,which can effectively improve the representation ability of local covariance descriptor under the single scale.Meanwhile,the locality-sensitive Riemannian kernel sparse representation-based classifier can utilize the locality of the multi-scale descriptor.The methods proposed in this paper are evaluated and compared with the results of some state-of-the-art approaches based on the public FRGC v2.0 and Bosphorus 3D face database.The experimental results prove the effectiveness of our algorithms,which lays the foundation for the better application of 3D face recognition.