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Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vectors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synthesizes these improvements. Experimental results on different image databases demonstrate that our improvements on LDA are efficient, and that ILDA outperforms other state-of-the-art linear discrimination methods.

In practice, huge amount of samples is readily available, while labeled samples are often very limited and too expensive to be easily obtained. Multi-view features usually reveal different types of traits of labeled and unlabeled samples. Semi-supervised multi-view learning is a learning paradigm designed to meet the requirement of learning from complementary information of multiple views of labeled and unlabeled samples. In this paper, we propose a semi-supervised multiple kernel intact discriminant space learning (SMKIDSL) method to discover latent intact feature representations for those samples. SMKIDSL employs correlation discriminant analysis and label regression to fully use class label information for enhancing the discriminant power of latent intact feature representations. In SMKIDSL, multi-view collaboration learning mechanism is utilized to efficiently integrate complementary information of multiple views, which enables optimal view being dominant in learning process. Besides, kernel technique is used to tackle nonlinear issue of original multi-view features for exploiting more discriminant information. Comprehensive experiments are conducted on Caltech 101, LFW, MNIST and RGB-D datasets. And the experimental results demonstrate the effectiveness and efficiency of our proposed method. The robustness of our method is also confirmed by those results. [ABSTRACT FROM AUTHOR]

Multi-view subspace learning has been aroused much concern recently. Although there exist a few multi-view subspace learning methods taking both the discrimination information and the correlation information into consideration, they always ignore the use of the inter-view discriminant information. In view of this, we propose an approach called multi-view local discrimination and canonical correlation analysis (MLDC 2 A) for image classification. MLDC 2 A aims to learn a common multi-view subspace from multi-view data, by making use of not only the discriminant information from both intra-view and inter-view but also the correlation information between paired view data. Furthermore, in the learned subspace, the local geometric structure of multi-view data is preserved. We conduct experiments on MNIST, COIL-20, Multi-PIE, Caltech-101, and COCO datasets and the results indicate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Discriminant subspace learning is a widely used feature extraction technique for image recognition, since it can extract effective discriminant features by employing the class information and Fisher criterion. A crucial research topic on this technique is how to rapidly extract sufficient and effective features. Recently, recursive discriminant subspace learning technique has attracted lots of research interest because it can acquire sufficient discriminant features. Generally, it recursively decomposes image samples and extracts features from a number of decomposed sample sets. The major drawback of most recursive discriminant subspace learning methods is that they calculate the projective vectors one by one, such that they suffer from big computational costs. The recursive modified linear discriminant method and the incremental recursive Fisher linear discriminant method employ a simple solution for this problem, which calculates the class number minus one projective vectors in each recursion. However, this solution produces the unfavorable projective vectors with poor discriminant capabilities, and it cannot provide the terminating criterion for recursive computation and make the projective vectors orthogonal. In this paper, we propose a novel recursive learning approach that is group recursive discriminant subspace learning, which can rapidly learn multiple orthogonal subspaces with each spanned by a group of projective vectors. And we present a rule to select favorable projective vectors per recursion and provide a matrix-form-based terminating criterion to determine the number of recursions. Experiments on three widely used databases demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Subspace learning is an effective and widely used image feature extraction and classification technique. However, for the large-scale image recognition issue in real-world applications, many subspace learning methods often suffer from large computational burden. In order to reduce the computational time and improve the recognition performance of subspace learning technique under this situation, we introduce the idea of parallel computing which can reduce the time complexity by splitting the original task into several subtasks. We develop a parallel subspace learning framework. In this framework, we first divide the sample set into several subsets by designing two random data division strategies that are equal data division and unequal data division. These two strategies correspond to equal and unequal computational abilities of nodes under parallel computing environment. Next, we calculate projection vectors from each subset in parallel. The graph embedding technique is employed to provide a general formulation for parallel feature extraction. After combining the extracted features from all nodes, we present a unified criterion to select most distinctive features for classification. Under the developed framework, we separately propose supervised and unsupervised parallel subspace learning approaches, which are called parallel linear discriminant analysis (PLDA) and parallel locality preserving projection (PLPP). PLDA selects features with the largest Fisher scores by estimating the weighted and unweighted sample scatter, while PLPP selects features with the smallest Laplacian scores by constructing a whole affinity matrix. Theoretically, we analyze the time complexities of proposed approaches and provide the fundamental supports for applying random division strategies. In the experiments, we establish two real parallel computing environments and employ four public image and video databases as the test data. Experimental results demonstrate that the proposed approaches outperform several related supervised and unsupervised subspace learning methods, and significantly reduce the computational time. [ABSTRACT FROM PUBLISHER]

Abstract: Manifold learning is an effective dimensional reduction technique for face feature extraction, which, generally speaking, tends to preserve the local neighborhood structures of given samples. However, neighbors of a sample often comprise more inter-class data than intra-class data, which is an undesirable effect for classification. In this paper, we address this problem by proposing a subclass-center based manifold preserving projection (SMPP) approach, which aims at preserving the local neighborhood structure of subclass-centers instead of given samples. We theoretically show from a probability perspective that, neighbors of a subclass-center would comprise of more intra-class data than inter-class data, and thus is more desirable for classification. In order to take full advantage of the class separability, we further propose the discriminant SMPP (DSMPP) approach, which incorporates the subclass discriminant analysis (SDA) technique to SMPP. In contrast to related discriminant manifold learning methods, DSMPP is formulated as a dual-objective optimization problem and we present analytical solution to it. Experimental results on the public AR, FERET and CAS-PEAL face databases demonstrate that the proposed approaches are more effective than related manifold learning and discriminant manifold learning methods in classification performance. [Copyright &y& Elsevier]

The principal component in the application of biometrics authentication (BA) technologies, the biometric data usually shows three characteristics: large numbers of individuals, small sample size and high dimensionality. One of the major research difficulties of BA is the single sample biometrics recognition problem. We often face this problem in real-world applications. It may lead to a bad recognition result. To solve this problem, we present a novel approach based on feature level biometrics fusion. We combine two kinds of biometrics: one is the face feature which is a representative of contactless biometrics, and another is the palmprint feature which is a typical contact biometrics. We extract the discriminant feature using Gabor-based image preprocessing and principal component analysis (PCA) techniques. And then design a distance-based separability weighting strategy to conduct feature level fusion. Using a large face database and a large palmprint database as the test data, the experimental results show that the presented approach significantly improves the recognition effect of single sample biometrics problem, and there is strong supplement between face and palmprint biometrics. [Copyright &y& Elsevier]

In this paper, new improvements for the linear discrimination technique are proposed. These improvements include effective solutions for the small sample size problem, the selection of appropriate principal component and more accurate within-class scatter estimation for the Fisher criterion. The effectiveness of our approach is proved by experimental results on the Yale face database. [Copyright &y& Elsevier]

With the increasing use of color images in the fields of pattern recognition, computer vision and machine learning, color face recognition technique becomes important, whose key problem is how to make full use of the color information and extract effective discriminating features. In this paper, we propose a novel nonlinear feature extraction approach for color face recognition, named dual multi-kernel discriminant analysis (DMDA), where we design a kernel selection strategy to select the optimal kernel mapping function for each color component of face images, further design a color space selection strategy to choose the most suitable space, then separately map different color components of face images into different high-dimensional kernel spaces, and finally perform multi-kernel learning and discriminant analysis not only within each component but also between different components. Experimental results in the public face recognition grand challenge (FRGC) version 2 and labeled faces in the wilds (LFW) databases illustrate that our approach outperforms several representative color face recognition methods. [ABSTRACT FROM AUTHOR]

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations. [ABSTRACT FROM AUTHOR]

Highlights • We extract view-independent features to remove the view discrepancy. • We integrate discriminant regularization to learn a discriminant subspace. • We extend single-view local geometry preservation to multi-view scenario. • We integrate local consistency regularization to learn a structured subspace. Abstract Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision. An effective solution to this problem is the multi-view subspace learning (MvSL), which intends to find a common subspace for multi-view data. Although great progress has been made, existing methods usually fail to find a suitable subspace when multi-view data lies on nonlinear manifolds, thus leading to performance deterioration. To circumvent this drawback, we propose Multi-view Common Component Discriminant Analysis (MvCCDA) to handle view discrepancy, discriminability and nonlinearity in a joint manner. Specifically, our MvCCDA incorporates supervised information and local geometric information into the common component extraction process to learn a discriminant common subspace and to discover the nonlinear structure embedded in multi-view data. Optimization and complexity analysis of MvCCDA are also presented for completeness. Our MvCCDA is competitive with the state-of-the-art MvSL based methods on four benchmark datasets, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

Palmprint contains stable and effective features, especially the orientation and scale features of palm lines, and has now become an important identity recognition technique for surveillance and safety applications. Existing palmprint recognition methods using texture features can be roughly divided into two categories: coding-based and local descriptor based methods. As compared with the latter category, the former one can make full use of the palmprint specific features and acquire fast matching speed. However, most existing coding-based methods are based on the competitive coding scheme, in which the scale features of palmprint cannot be well exploited. In this work, we propose a discriminant orientation and scale features learning (DOSFL) for palmprint recognition. By introducing the idea of discriminant analysis into palmprint coding, DOSFL can extract the orientation and scale features with more favorable discriminability. Then, DOSFL utilizes four code bits to represent both the orientation and scale features of palmprint, and employs the Hamming distance for code matching. To make better use of the orientation and scale information contained in palmprint samples, we further propose a multi-orientation and multi-scale features discriminant learning (MOSDL) approach for palmprint recognition, which can fuse different orientation and scale feature information effectively in the discriminant learning process. Experimental results on two publicly available palmprint databases, including the HK PolyU database and UST palmprint image database, demonstrate that our proposed approach can achieve better recognition results than the compared methods. [ABSTRACT FROM AUTHOR]

Manifold learning aims to discover the low dimensional space where the input high dimensional data are embedded by preserving the geometric structure. Unfortunately, almost all the existing manifold learning methods were proposed under single view scenario, and they cannot be straightforwardly applied to multiple feature sets. Although concatenating multiple views into a single feature provides a plausible solution, it remains a question on how to better explore the independence and interdependence of different views while conducting manifold learning. In this paper, we propose a multi-view manifold learning with locality alignment (MVML-LA) framework to learn a common yet discriminative low-dimensional latent space that contain sufficient information of original inputs. Both supervised algorithm (S-MVML-LA) and unsupervised algorithm (U-MVML-LA) are developed. Experiments on benchmark real-world datasets demonstrate the superiority of our proposed S-MVML-LA and U-MVML-LA over existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]