Discriminant Analysis For Face Recognition Research Articles

Collaborative Representation Based Graph Embedding Discriminant Analysis for Face Recognition

Considering the collaborative representation projection constructs the adjacent graph based on the collaborative representation relationship of samples and many samples from different classes may be gathered together after projection, a discriminative competitive and collaborative representation projection is proposed. Firstly, each sample is competitively and collaboratively represented by all samples in the dataset to calculate the similarity of samples. Then, an intraclass graph is constructed to characterize the intraclass compactness, and an interclass graph is built to characterize the interclass separability. On this basis, the label propagation algorithm is applied to calculate the soft label information of unlabeled samples to eliminate the influence of unlabeled samples on the recognition results. Additionally, the nonlinear mapping is used to replace the linear inner in the graph embedding framework to solve the linearly inseparable problem of the original samples in the low-dimensional space. Experimental results on ORL, AR, FERET and Yale B face datasets show that compared to the CRP method, the proposed method improves the maximum recognition rate by 1%~4% and improves 2%~6% in noise and blur results, respectively.

Collaborative representation-based fuzzy discriminant analysis for Face recognition

Collaborative representation-based fuzzy discriminant analysis for Face recognition

Two-dimensional Subclass Discriminant Analysis for face recognition

Dimensionality reduction plays a major role in face recognition. Discriminant analysis (DA) and principal component analysis (PCA) are two of the most important approaches in this field. In particular, subclass discriminant analysis (SDA) is a well-known scheme for feature extraction and dimensionality reduction. It is widely used in many high-dimensional data-driven applications, namely face recognition and image retrieval. It is also found to be applicable under various scenarios. However, it has high cost in time and space given the need for an eigendecomposition involving the scatter matrices, known as the singularity problem. This limitation is caused by the high-dimensional space of data, particularly when dimensions exceed the number of observations. Recent advances widely reported that 2D methods with matrix-based representation perform better than the traditional 1D vector-based ones. In this paper, we propose a novel 2D-SDA algorithm to avoid the “curse of dimensionality” and address the singularity issue. The performance of the proposed algorithm is evaluated for face recognition in terms of recognition performance and computational cost. Experiments are conducted on four benchmark face databases and compared to several competitive 1D and 2D methods based on PCA and DA. Results show that 2DSVD achieves the best recognition performance at low dimensions. In particular, 2D-SDA works significantly better on large-sized data sets where intra-class variation is the most important.

Locality preserving partial least squares discriminant analysis for face recognition

We propose a locality preserving partial least squares discriminant analysis (LPPLSDA) which adds a locality preserving feature to the conventional partial least squares discriminant analysis(PLS-DA). The locality preserving feature captures the within group structural information via a similarity graph. The ability of LPPLS-DA to capture local structures allows it to be better suited for face recognition. We evaluate the performance of our proposed method on several benchmarked face databases which offer different levels of complexity in terms of sample size as well as image acquisition conditions. The experimental results indicate that, for each database used, the proposed method consistently outperformed the conventional PLS-DA method.

A novel fuzzy linear discriminant analysis for face recognition

A novel fuzzy linear discriminant analysis for face recognition

A Regularized Locality Projection-Based Sparsity Discriminant Analysis for Face Recognition

Manifold learning and classifiers based on sparse representation are widely used in pattern recognition. Most of the conventional manifold learning methods are subjected to the choice of parameters. In this paper, we present a Regularized Locality Projection based on Sparsity Discriminant Analysis (RLPSD) method for Feature Extraction (FE) to understand the high-dimensional data such as face images. In RLPSD, firstly, we show the sparse representation of training samples by collaborative representation-based classification (CRC). Secondly, the idea of part optimization based on sparse representation is used to ensure the within-class compactness which combines with the labels of measurements and the weights of sparse presentation can be as small as possible. Finally, whole optimization can be directly obtained without the iteration of local optimization. Meanwhile, the separability information of between-class can be well discriminated by scatter matrix which is similar to Fisher linear discriminant analysis (LDA). The great recognition performance of the proposed method is verified by comparing with the popular algorithms on Yale, ORL, AR and Extended YaleB face databases and Oxford 102 flowers dataset.

A kernel approach to implementation of local linear discriminant analysis for face recognition

The multiple linear model is used successfully to extend the linear model to nonlinear problems. However, the conventional multilinear models fail to learn the global structure of a training data set because the local linear models are independent of each other. Furthermore, the local linear transformations are learned in the original space. Therefore, the performance of multilinear methods is strongly dependent on the results of partition. This paper presents a kernel approach for the implementation of the local linear discriminant analysis for face recognition problems. In the original space, we utilize a set of local linear transformations with interpolation to approximate an optimal global nonlinear transformation. Based on the local linear models in the original space, we derive an explicit kernel mapping to map the training data into a high‐dimensional transformed space. The optimal transformation is learned globally in the transformed space. Experimental results show that the proposed method is more robust to the partition results than the conventional multilinear methods. Compared with the general nonlinear kernels that utilize a black‐box mapping, our proposed method can reduce the negative effects caused by the potential overfitting problem. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Orthogonal enhanced linear discriminant analysis for face recognition

From the intuition that natural face images lie on or near a low-dimensional submanifold, the authors propose a novel spectral graph based dimensionality reduction method, named orthogonal enhanced linear discriminant analysis (OELDA), for face recognition. OELDA is based on enhanced LDA (ELDA), which takes into account both the discriminative structure and geometrical structure of the face space, and generates non-orthogonal basis vectors. However, a significant fact is that eliminating the dependence of basis vectors can promote more effective recognition of unseen face images. For this purpose, the authors seek to improve the ELDA scheme by imposing orthogonal constraints on the basis vectors. Experimental results on real-world face datasets show that, benefitting from orthogonality, OELDA has more locality preserving power and discriminative power than LDA and ELDA, and achieves the highest recognition rates among compared methods.

Local Similarity based Discriminant Analysis for Face Recognition

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the “divide-conquer” strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition.

Robust descriptor-based subspace learning with complex data is an active topic in pattern analysis and machine intelligence. A few researches concentrate the optimal design on feature representation and metric learning. However, traditionally used features of single-type, e.g., image gradient orientations (IGOs), are deficient to characterize the complete variations in robust and discriminant subspace learning. Meanwhile, discontinuity in edge alignment and feature match are not been carefully treated in the literature. In this paper, local order constrained IGOs are exploited to generate robust features. As the difference-based filters explicitly consider the local contrasts within neighboring pixel points, the proposed features enhance the local textures and the order-based coding ability, thus discover intrinsic structure of facial images further. The multimodal features are automatically fused in the most discriminant subspace. The utilization of adaptive interaction function suppresses outliers in each dimension for robust similarity measurement and discriminant analysis. The sparsity-driven regression model is modified to adapt the classification issue of the compact feature representation. Extensive experiments are conducted by using some benchmark face data sets, e.g., of controlled and uncontrolled environments, to evaluate our new algorithm.

Graph Regularized Sparsity Discriminant Analysis for face recognition

Manifold learning and Sparse Representation Classifier are two popular techniques for face recognition. Because manifold learning can find low-dimensional representations for high-dimensional data, it is widely applied in computer vision and pattern recognition. Most of the manifold learning algorithms can be unified in the graph embedding framework, where the first step is to determine the adjacent graphs. Traditional methods use k nearest neighbor or the ε-ball schemes. However, they are parametric and sensitive to noises. Moreover, it is hard to determine the size of appropriate neighborhoods. To deal with these problems, in this paper, Graph Regularized Sparsity Discriminant Analysis, GRSDA, for short, is proposed. Based on graph embedding and sparsity preserving projection, the weight matrices for intrinsic and penalty graphs are obtained through sparse representation. GRSDA seeks a subspace in which samples in intra-classes are as compact as possible while samples in inter-classes are as separable as possible. Specifically, samples in the low-dimensional space can preserve the sparse locality relationship in the same class, while enhancing the separability for samples in different classes. Hence, GRSDA can achieve better performance. Extensive experiments were carried out on ORL, YALE-B and AR face databases, and the results confirmed that the proposed algorithm outperformed LPP, UDP, SPP and DSNPE.

Facial Recognition Using Square Diagonal Matrix Based on Two-Dimensional Linear Discriminant Analysis

In this research, the square diagonal matrix based on Two-Dimensional Linear Discriminant Analysis for Face recognition is proposed. The original image matrix is converted into the diagonal matrix and followed the same process by using the output become input. The results of the conversion are utilized as input on feature extraction using Two-Dimensional Linear Discriminant Analysis. The proposed method has been evaluated by using the YALE-A, the ORL and the University of Bern Face image databases. The experimental results show that, the proposed method outperformed to other methods, which are Eigen Faces, Fisher Faces, Laplacian Faces and Orthogonal Laplacian Faces methods. The highest recognition rates are 89.523%, 97% and 95.33% for the YALE-A, the ORL, and the University of Bern respectively. The usage of the dominant features has influenced the results of the recognition. It is proved that, the more the dominant features, the smaller error occurred.

얼굴인식을 위한 판별분석에 기반한 복합특징 벡터 구성 방법

We propose a method to construct composite feature vector based on discriminant analysis for face recognition. For this, we first extract the holistic- and local-features from whole face images and local images, which consist of the discriminant pixels, by using a discriminant feature extraction method. In order to utilize both advantages of holistic- and local-features, we evaluate the amount of the discriminative information in each feature and then construct a composite feature vector with only the features that contain a large amount of discriminative information. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed composite feature vector has improvement of face recognition performance.

Study of Similarity Measures with Linear Discriminant Analysis for Face Recognition

Face recognition systems have been in the active research in the area of image processing for quite a long time. Evaluating the face recognition system was carried out with various types of algorithms used for extracting the features, their classification and matching. Similarity measure or distance measure is also an important factor in assessing the quality of a face recognition system. There are various distance measures in literature which are widely used in this area. In this work, a new class of similarity measure based on the Lp metric between fuzzy sets is proposed which gives better results when compared to the existing distance measures in the area with Linear Discriminant Analysis (LDA). The result points to a positive direction that with the existing feature extraction methods itself the results can be improved if the similarity measure in the matching part is efficient.

Two-Dimensional Weighted and Locality Preserved Discriminant Analysis for Face Recognition

In this paper, a novel method for face recognition, called two-dimensional weighted and locality preserved discriminant analysis (2D-WLPDA) is proposed. The new algorithm is developed based on three techniques: (1) locality preserved embedding, by embedding nearest-neighbor graphs which characterize the within-class compactness of the same class samples, 2D-WLPDA discovers the submanifold of images space; (2) image based projection which can avoids the small sample problem and improves the computation efficiency;(3) weighting contributions of individual class pairs which alleviates the overlap of neighboring classes in Fisher criterion for a k-class problem with k>2. We experimentally compare 2D-WLPDA to other feature extraction methods, such as 2D-LDA, 2D-PCA and 2D-DLPP, 2D-WLPDA has better recognition performance.

Pairwise Costs in Semisupervised Discriminant Analysis for Face Recognition

In recent years, face recognition is being recognized as a cost-sensitive learning problem. Many cost-sensitive classifiers have been proposed. However, no sufficient attention is paid to the research on cost-sensitive dimensionality reduction, especially on the cost-sensitive semisupervised dimensionality reduction. To the best of our knowledge, cost sensitive semisupervised discriminant analysis (CS3DA) may be the first work. CS3DA first uses the sparse representation to infer a soft label for unlabeled sample and then learns the projection direction by incorporating misclassification costs into both labeled and unlabeled data. Although CS3DA reduces the loss of misclassification, it has two major drawbacks: 1) the sparsity is not a feature of face recognition, and therefore sparse approximations may not deliver the robustness or performance desired and 2) CS3DA is not proven to satisfy the minimal misclassification loss criterion. In this paper, we embed pairwise costs in semisupervised discriminant analysis (PCSDA) for face recognition. PCSDA first uses a simple \(l_{2}\) approach to predict the label of unlabeled data, and then learns the projection direction by embedding pairwise costs in both labeled and unlabeled data. Compared with CS3DA, PCSDA has three major advantages: 1) \(l_{2}\) approach is more accurate and robust than sparse representation for face recognition; 2) we prove that CS3DA approximates the pairwise Bayesian risk only when the classes are balanced and without outliers in face data sets; and 3) PCSDA approximates the pairwise Bayesian risk considering the class imbalance problem and outliers in face recognition. Hence, the projection direction obtained by using PCSDA can be more discriminative, immunes to outliers and class imbalance problem. The experimental results on AR, PIE, ORL, and extended Yale B data sets demonstrate the effectiveness of PCSDA.

Uncorrelated regularized local Fisher discriminant analysis for face recognition

A local Fisher discriminant analysis can work well for a multimodal problem. However, it often suffers from the undersampled problem, which makes the local within-class scatter matrix singular. We develop a super- vised discriminant analysis technique called uncorrelated regularized local Fisher discriminant analysis for image feature extraction. In this technique, the local within-class scatter matrix is approximated by a full- rank matrix that not only solves the undersampled problem but also eliminates the poor impact of small and zero eigenvalues. Statistically uncorrelated features are obtained to remove redundancy. A trace ratio criterion and the corresponding iterative algorithm are employed to globally solve the objective function. Experimental results on four famous face databases indicate that our proposed method is effective and outperforms the con- ventional dimensionality reduction methods. © 2014 SPIE and IS&T (DOI: 10.1117/1.JEI.23.4.043017)

Dual-kernel based 2D linear discriminant analysis for face recognition

This paper proposes a new image feature extraction method for face recognition, called dual-kernel based two dimensional linear discriminant analysis (D-K2DLDA), by integrating multiple kernel discriminant analysis with the existing K2DFDA method. The proposed method deals with a face image directly as a matrix, instead of a stacked vector from rows or columns of the image. Moreover, we separately perform an iterative scheme for kernel parameter optimization for each of the two kernels, based on the maximum margin criterion and the damped Newton’s method, followed by a fusion procedure of the two kernels. Experimental results on the ORL and UMIST face databases show the effectiveness of D-K2DLDA.

Kernel inverse Fisher discriminant analysis for face recognition

In this paper, we present a new nonlinear feature extraction method for face recognition. The proposed method incorporates the kernel trick with inverse Fisher discriminant analysis and develops a two-phase kernel inverse Fisher discriminant analysis criterion – KPCA plus IFDA. In the proposed method, we first apply the nonlinear kernel trick to map the original face samples into an implicit feature space and then perform inverse Fisher discriminant analysis in the feature space to produce nonlinear discriminating features. In implementation, kernel IFDA seeks nonlinear discriminating features by minimizing the inverse Fisher discriminant quotient and overcome the singularity problem by projective transformation of scatter matrices. Experimental results on ORL, FERET and AR face databases demonstrate the effectiveness of the proposed method.

Discriminant embedding by sparse representation and nonparametric discriminant analysis for face recognition

A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis (DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms.