Research On Several Algorithms For Face Recognition

Face recognition is a key subject of the research on biometrics.It relates to image processing,pattern recognition,computer vision,statistical learning,cognitive science and psychology,and many other important disciplines.It is also in great need and necessary of national security and public safety,and becomes one of the hottest issues in that field.Due to great academic research value and prospects of a wide range of applications,the research of effective and efficient face recognition algorithms has become one of the most attractive and challenging tasks in biometrics,and has gained wide attentions by researchers both at home and abroad for years.Effective feature extraction is one of the core technologies for face recognition.To extract the most distinctive face features of the same individual,which is different from the general public,is the first and crucial phase for a highly efficient face recognition algorithm.In this dissertation,facial feature extraction and pattern classification for the human faces have been studied and discussed.Aiming at the difficulty of the traditional appearance-based pose estimation,several new approaches are proposed under the frame of statistic learning.The main research content and innovative work are as follows:1.Two kinds of face recognition algorithms which aim at enhancing the locality preserving performance are proposed based on the research of Locality Preserving Projections(LPP).1) A Supervised locality preserving projections under Bi-directional Compression Transformation(SLPP-BCT) algorithm is proposed for face recognition.The traditional LPP represents a face image by a vector in high-dimensional space which leads to the difficulty of feature extraction and is easily confronted with the matrix singular problem and high computational complexity.In this new proposed method,the bilateral-projection-based 2DPCA(2D~2 PCA ) algorithm is used to remove the redundancy from two directions of the image.It preserves the face image structure as a whole,meanwhile,it directly preserve the local information of the compressed data space under a surprised mode. Experiments demonstrate the effectiveness and efficiency of the new proposed method.It outperforms some most popular algorithms on both recognition speed and accuracy.2) A new method called kernel based orthogonal locality preserving projections algorithm is proposed for face representation and recognition.In this method, the nonlinear kernel mapping is used to map the face data into an implicit feature space,and then a linear transformation which produces orthogonal basis functions is performed to preserve locality geometric structures of the face image.KOLPP is performed under a supervised learning mode which improved the locality preserving capacity of the face samples,and the orthogonalization constraints enhanced the discriminated features extraction between different individuals,simultaneously.Therefore,KOLPP algorithm preserves the nonlinear geometric structures of face image better and obtains a more satisfactory recognition performance.2.Two novel appearance-based methods,called Orthogonal Locality Sensitive Discriminant Analysis and Tensor-based Orthogonal Locality Sensitive Discriminant Analysis(Tensor OLSDA),are proposed based on the analysis of the newly proposed Locality Sensitive Discriminant Analysis(LSDA) algorithm.1) OLSDA projects the face data into a linear subspace which maximized the margin constructed by data points from the same class and the different classes at each local neighborhood,so that preserves not only the local neighborhood information but discriminant information as well.Furthermore,the orthogonal basis function based constraint is added into the objective function of LSDA to emphasize the discriminant information.Orthogonal LSDA algorithm is proposed to preserve the local geometrical structure by computing the mutually orthogonal basis functions iteratively.Experimental results also proved its validity and stability.2) Motivated by the Locality Sensitive Discriminant Analysis(LSDA),a novel appearance-based method that called Tensor Orthogonal Locality Sensitive Discriminant Analysis(Tensor OLSDA) is presented for face recognition.With face data's high-order tensor representation,this new method preserves its spatial structure of the face image better,which is actually in a 2-D vector form. Tensor-based representation doesn't need to expand the face data into a high-dimensional space which avoids the problem of singular matrix effectively.Experimental results also show the impressive performance of the proposed method.3.The Rotate and shift invariant based United Subspace Analysis(Rotate and Shift Invariant-based USA) is proposed in this thesis.A fast and effective new method,called Rotate and Shift Invariant based United Subspace Analysis,is proposed for the pose and distance changed face recognition in this paper.In the proposed method,the local characteristics and detail texture information enhanced Gabor feature is first compressed by the 2D~2 PCA algorithm to extract the global feature and reduce dimension of the face features.Then the core algorithmâ… (United-SLPP) or the core algorithmâ…¡(United-OLSDA) are utilized for further feature extraction,respectively.Moreover,the united subspace algorithm is tested and analyzed with the experiments on two face databases of different scales.The results show that,the proposed Rotate and Shift Invariant based United Subspace Analysis takes the comprehensive advantages of the algorithms proposed above and can significantly improve the accuracy of face recognition in different occasions.