Bidirectional representation for face recognition across pose

Conventional representation methods try to express the test sample as a weighting sum of training samples and exploit the deviation between the test sample and the weighting sum of the training samples from each class (also referred to as deviation between the test sample and each class) to classify the test sample. In particular, the methods assign the test sample to the class that has the smallest deviation among all the classes. This paper analyzes the relationship between face images under different poses and, for the first time, devises a bidirectional representation method-based pattern classification (BRBPC) method for face recognition across pose. BRBPC includes the following three steps: the first step uses the procedure of conventional representation methods to express the test sample and calculates the deviation between the test sample and each class. The second step first expresses the training sample of a class as a weighting sum of the test sample and the training samples from all the other classes and then obtains the corresponding deviation (referred to as complementary deviation). The third step uses the score-level fusion to integrate the scores, that is, deviations generated from the first and second steps for final classification. The experimental results show that BRBPC classifies more accurately than conventional representation methods.     

Orthogonal neighborhood preserving discriminant analysis for face recognition

In this paper, we propose a new linear subspace analysis algorithm, called orthogonal neighborhood preserving discriminant analysis (ONPDA). Given a set of data points in the ambient space, a weight matrix is firstly built which describes the relationship between the data points. Then optimal between-class scatter matrix and within-class scatter matrix are defined such that the neighborhood structure can be preserved. In order to improve the discriminating power, a new method is presented for orthogonalizing the basis eigenvectors. We evaluate the performance of the proposed algorithm for face recognition with the use of different databases. Consistent and promising results demonstrate the effectiveness of our algorithm.     

Tied factor analysis for face recognition across large pose differences

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized "identity" space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model "tied" factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric which allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance using the FERET, XM2VTS and PIE databases. Recognition performance compares favourably to contemporary approaches.     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.     

Orthogonal local spline discriminant projection with application to face recognition

In this paper, an efficient feature extraction algorithm called orthogonal local spline discriminant projection (O-LSDP) is proposed for face recognition. Derived from local spline embedding (LSE), O-LSDP not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. Extensive experiments on several standard face databases demonstrate the effectiveness of the proposed method.     

Two-dimensional discriminant transform for face recognition

This paper develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition.     

Regularized discriminant analysis for face recognition

This paper studies regularized discriminant analysis (RDA) in the context of face recognition. We check RDA sensitivity to different photometric preprocessing methods and compare its performance to other classifiers. Our study shows that RDA is better able to extract the relevant discriminatory information from training data than the other classifiers tested, thus obtaining a lower error rate. Moreover, RDA is robust under various lighting conditions while the other classifiers perform badly when no photometric method is applied.     

Graph embedding discriminant analysis for face recognition

This paper develops a supervised discriminant technique, called graph embedding discriminant analysis (GEDA), for dimensionality reduction of high-dimensional data in small sample size problems. GEDA can be seen as a linear approximation of a multimanifold-based learning framework in which nonlocal property is taken into account besides the marginal property and local property. GEDA seeks to find a set of perfect projections that not only can impact the samples of intraclass and maximize the margin of interclass, but also can maximize the nonlocal scatter at the same time. This characteristic makes GEDA more intuitive and more powerful than linear discriminant analysis (LDA) and marginal fisher analysis (MFA). The proposed method is applied to face recognition and is examined on the Yale, ORL and AR face image databases. The experimental results show that GEDA consistently outperforms LDA and MFA when the training sample size per class is small.     

Kernel optimization-based discriminant analysis for face recognition

The selection of kernel function and its parameter influences the performance of kernel learning machine. The difference geometry structure of the empirical feature space is achieved under the different kernel and its parameters. The traditional changing only the kernel parameters method will not change the data distribution in the empirical feature space, which is not feasible to improve the performance of kernel learning. This paper applies kernel optimization to enhance the performance of kernel discriminant analysis and proposes a so-called Kernel Optimization-based Discriminant Analysis (KODA) for face recognition. The procedure of KODA consisted of two steps: optimizing kernel and projecting. KODA automatically adjusts the parameters of kernel according to the input samples and performance on feature extraction is improved for face recognition. Simulations on Yale and ORL face databases are demonstrated the feasibility of enhancing KDA with kernel optimization.     

Kernel-based nonlinear discriminant analysis for face recognition

Linear subspace analysis methods have been successfully applied to extract features for face recognition.But they are inadequate to represent the complex and nonlinear variations of real face images,such as illumination,facial expression and pose variations,because of their linear properties.In this paper,a nonlinear subspace analysis method,Kernel-based Nonlinear Discriminant Analysis (KNDA),is presented for face recognition,which combines the nonlinear kernel trick with the linear subspace analysis method-Fisher Linear Discriminant Analysis (FLDA).First,the kernel trick is used to project the input data into an implicit feature space,then FLDA is performed in this feature space.Thus nonlinear discriminant features of the input data are yielded.In addition,in order to reduce the computational complexity,a geometry-based feature vectors selection scheme is adopted.Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA),which combines the kernel trick with linear Principal Component Analysis (PCA).Experiments are performed with the polynomial kernel,and KNDA is compared with KPCA and FLDA.Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.     

增强的无监督人脸鉴别技术

增强的独立分量分析（EICA）是一种基于样本整体特征的无监督特征抽取方法,并没有考虑样本的局部特征,因此EICA不利于处理人脸识别这类非线性问题的。无监督鉴别投影技术（UDP）用于高维数据压缩,其基本思想是寻找一组有效的投影方向,使得样本投影后,局部散度最小同时非局部散度最大。UDP同时考虑到样本的局部特征和非局部特征,能够反映样本内在的数据关系,因此UDP能够对样本有效地分类。提出了一种增强的无监督人脸鉴别技术,该方法结合了EICA和UDP的优点,能够：（1）反映样本高阶统计特征;（2）发掘样本内在的几何结构,从而有利于分类。在Yale人脸库和FERET人脸库上的实验验证了该算法的有效性。     

Line-based face recognition under varying pose

Much research in human face recognition involves fronto-parallel face images, constrained rotations in and out of the plane, and operates under strict imaging conditions such as controlled illumination and limited facial expressions. Face recognition using multiple views in the viewing sphere is a more difficult task since face rotations out of the imaging plane can introduce occlusion of facial structures. In this paper, we propose a novel image-based face recognition algorithm that uses a set of random rectilinear line segments of 2D face image views as the underlying image representation, together with a nearest-neighbor classifier as the line matching scheme. The combination of 1D line segments exploits the inherent coherence in one or more 2D face image views in the viewing sphere. The algorithm achieves high generalization recognition rates for rotations both in and out of the plane, is robust to scaling, and is computationally efficient. Results show that the classification accuracy of the algorithm is superior compared with benchmark algorithms and is able to recognize test views in quasi-real-time     

Local uncorrelated local discriminant embedding for face recognition

The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding (LULDE). The proposed approach can be seen as an extension of a local discriminant embedding (LDE) framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.     

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.     

Kernel relevance weighted discriminant analysis for face recognition

In this paper, we propose a new kernel discriminant analysis called kernel relevance weighted discriminant analysis (KRWDA) which has several interesting characteristics. First, it can effectively deal with the small sample size problem by using a QR decomposition on scatter matrices. Second, by incorporating a weighting function into discriminant criterion, it overcomes overemphasis on well-separated classes and hence can work under more realistic situations. Finally, using kernel theory, it handle non linearity efficiently. In order to improve performance of the proposed algorithm, we introduce two novel kernel functions and compare them with some commonly used kernels on face recognition field. We have performed multiple face recognition experiments to compare KRWDA with other dimensionality reduction methods showing that KRWDA consistently gives the best results.     

Ensemble-based discriminant learning with boosting for face recognition

In this paper, we propose a novel ensemble-based approach to boost performance of traditional Linear Discriminant Analysis (LDA)-based methods used in face recognition. The ensemble-based approach is based on the recently emerged technique known as "boosting". However, it is generally believed that boosting-like learning rules are not suited to a strong and stable learner such as LDA. To break the limitation, a novel weakness analysis theory is developed here. The theory attempts to boost a strong learner by increasing the diversity between the classifiers created by the learner, at the expense of decreasing their margins, so as to achieve a tradeoff suggested by recent boosting studies for a low generalization error. In addition, a novel distribution accounting for the pairwise class discriminant information is introduced for effective interaction between the booster and the LDA-based learner. The integration of all these methodologies proposed here leads to the novel ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA techniques. Promising experimental results obtained on various difficult face recognition scenarios demonstrate the effectiveness of the proposed approach. We believe that this work is especially beneficial in extending the boosting framework to accommodate general (strong/weak) learners.     

Incremental linear discriminant analysis for face recognition.

Dimensionality reduction methods have been successfully employed for face recognition. Among the various dimensionality reduction algorithms, linear (Fisher) discriminant analysis (LDA) is one of the popular supervised dimensionality reduction methods, and many LDA-based face recognition algorithms/systems have been reported in the last decade. However, the LDA-based face recognition systems suffer from the scalability problem. To overcome this limitation, an incremental approach is a natural solution. The main difficulty in developing the incremental LDA (ILDA) is to handle the inverse of the within-class scatter matrix. In this paper, based on the generalized singular value decomposition LDA (LDA/GSVD), we develop a new ILDA algorithm called GSVD-ILDA. Different from the existing techniques in which the new projection matrix is found in a restricted subspace, the proposed GSVD-ILDA determines the projection matrix in full space. Extensive experiments are performed to compare the proposed GSVD-ILDA with the LDA/GSVD as well as the existing ILDA methods using the face recognition technology face database and the Carneggie Mellon University Pose, Illumination, and Expression face database. Experimental results show that the proposed GSVD-ILDA algorithm gives the same performance as the LDA/GSVD with much smaller computational complexity. The experimental results also show that the proposed GSVD-ILDA gives better classification performance than the other recently proposed ILDA algorithms.     

Regularized locality preserving discriminant embedding for face recognition

For face recognition, graph embedding techniques attempt to produce a high data locality projection for better recognition performance. However, estimation of population data locality could be severely biased due to small number of training samples. The biased estimation triggers overfitting problem and hence poor generalization. In this paper, we propose a new linear graph embedding technique based upon an adaptive locality preserving regulation model (ALPRM), known as Regularized Locality Preserving Discriminant Embedding (RLPDE). In RLPDE, the projection features are regulated based on ALPRM to approach population data locality, which can directly enhance the locality preserving capability of the projection features. This paper also presents the relation between locality preserving capability and class discrimination. Specifically, we show that the optimization of the locality preserving function minimizes the within-class variability. Experiments on three face datasets such as PIE, FRGC and FERET show the promising performance of the proposed technique.