Xiaohua Gu

Letters: Null space discriminant locality preserving projections for face recognition

In this paper, we propose a null space discriminant locality preserving projections (NDLPP) method for facial feature extraction and recognition. Based on locality preserving projections (LPP) and discriminant locality preserving projections (DLPP) methods, NDLPP comes into the characteristics of DLPP that encodes both the geometrical and discriminant structure of the data manifold, and addresses the small sample size problem by solving an eigenvalue problem in null space. Experiments on synthetic data and ORL, Yale, and FERET face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of NDLPP.

Bagging null space locality preserving discriminant classifiers for face recognition

In this paper, we propose a novel bagging null space locality preserving discriminant analysis (bagNLPDA) method for facial feature extraction and recognition. The bagNLPDA method first projects all the training samples into the range space of a so-called locality preserving total scatter matrix without losing any discriminative information. The projected training samples are then randomly sampled using bagging to generate a set of bootstrap replicates. Null space discriminant analysis is performed in each replicate and the results of them are combined using majority voting. As a result, the proposed method aggregates a set of complementary null space locality preserving discriminant classifiers. Experiments on FERET and PIE subsets demonstrate the effectiveness of bagNLPDA.

Regularized locality preserving discriminant analysis for face recognition

This paper proposes a regularized locality preserving discriminant analysis (RLPDA) approach for facial feature extraction and recognition. The RLPDA approach decomposes the eigenspace of the locality preserving within-class scatter matrix into three subspaces, i.e., the face space, the noise space and the null space, and then regularizes the three subspaces differently according to their predicted eigenvalues. As a result, the proposed approach integrates discriminative information in all of the three subspaces, de-emphasizes the effect of the eigenvectors corresponding to the small eigenvalues, and meanwhile suppresses the small sample size problem. Extensive experiments on ORL face database, FERET face subset and UMIST face database illustrate the effectiveness of the proposed approach.

Regularized Kernel Locality Preserving Discriminant Analysis for Face Recognition

In this paper, a regularized kernel locality preserving discriminant analysis (RKLPDA) method is proposed for facial feature extraction and recognition. The proposed RKLPDA comes into the characteristic of LPDA that encodes both the geometrical and discriminant structure of the data manifold, and improves the classification ability for linear non-separable data by introducing kernel trick. Meanwhile, by regularizing the eigenvectors of the kernel locality preserving within-class scatter, RKLPDA utilizes all the discriminant information and eliminates the small sample size (SSS) problem. Experiments on ORL and FERET face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of RKLPDA.

Facial Feature Selection Based on SVMs by Regularized Risk Minimization

In this paper we present a method based on SVMs by regularized risk minimization for the facial feature selection aiming at improving performance of the classifier by (1) using WT + KPCA as filter approach to choose a set of more meaningful representatives to replace the original data for feature selection; (2) using SVM RFE iterative procedure as wrapper approach to obtain the optimum feature subset; (3) using regularized risk minimization as feature selection ranking criterion. Experimental results on FERET face database subsets indicate that the proposed method has a significant improvement in the classification accuracy and speed

Extended Locality Preserving Discriminant Analysis for Face Recognition

In this paper, an extended locality preserving discriminant analysis (ELPDA) method is proposed. To address the disadvantages of original locality preserving discriminant analysis (LPDA), a new locality preserving between-class scatter, which is characterized by samples and the corresponding k out-class nearest neighbors, is defined. Moreover, the small sample size problem is also avoided by solving a new optimization function. Experimental results on AR and FERET subsets illustrate the effectiveness of the proposed method for face recognition.

A face wrapping method based on pose-specific shape eigenspace

Generating virtual face images with different poses has potential applications in many areas, such as face recognition, human-machine interaction, portrait combination, and computer graphics. However, in some situation, the available face images are quite limited, which makes the problem difficult. This paper proposes a pose-specific shape eigenspace based face wrapping method to generate virtual face images with different poses from a specific pose. A predefined training set is necessary. According to their poses, training faces with annotated landmarks are manually divided into several groups, each of which is utilized to learn a pose-specific shape eigenspace by K-L transform. For a new image under a certain pose, its shape information described by the annotated landmarks is firstly projected to the expected pose-specific shape eigenspace to represent the shape information of this image under the expected pose. Then, all corresponding points between the represented shape and original shape the are matched and the texture information of all points in the represented shape are covered by the gray or color information of the corresponding points in the original image to generate a virtual face image under expected pose. To quantify the similarity between the generated virtual images and real images, cosine similarity is adopted. Experiments on IMM, PIE and YaleB face subsets show that the similarity of the virtual image and real images is over 0.9, no matter there is high or low similarity between test set and training set, which illustrates the effectiveness of the proposed method.

Reliability Pyramid Based Dense Corresponding between Facial Texture Images

Dense corresponding between facial texture images is the core step of the morphable model based 3D face reconstruction. In this paper, we propose an original dense corresponding algorithm by means of the reliability pyramid, which utilizes the output of highly accurate sparse matching algorithm as the initial estimation of the iterative Lucas-Kanade optical flow. Furthermore, two constraints for facial texture corresponding and a rapid 3D face reconstruction strategy are also briefly introduced. We conduct experiments on the BJUT-3D Face Database, proving the above-mentioned theories to be powerful.