The traditional data uncertainty (DU) classifier fails to encode the importance of each sample for solving the minimum problem. Moreover, it considers only linear information for classification. To overcome these, we propose four classifiers for action recognition. They are called regularized DU (RDU) classifier, RDU coefficient (RDUC) classifier, kernel RDU (KRDU) classifier, and kernel RDUC (KRDUC) classifier, respectively. Extensive experiments on four benchmark action databases demonstrate that the proposed four classifiers achieve better recognition rates than the traditional DU classifier and several state-of-the-art methods. Moreover, the computation costs of the KRDU and KRDUC classifiers are much less than that of the DU classifier.

In this paper, a novel classifier, called superimposed sparse parameter (SSP) classifier is proposed for face recognition. SSP is motivated by two phase test sample sparse representation (TPTSSR) and linear regression classification (LRC), which can be treated as the extended of sparse representation classification (SRC). SRC uses all the train samples to produce the sparse representation vector for classification. The LRC, which can be interpreted as L2-norm sparse representation, uses the distances between the test sample and the class subspaces for classification. TPTSSR is also L2-norm sparse representation and uses two phase to compute the distance for classification. Instead of the distances, the SSP classifier employs the SSPs, which can be expressed as the sum of the linear regression parameters of each class in iterations, is used for face classification. Further, the fast SSP (FSSP) classifier is also suggested to reduce the computation cost. A mass of experiments on Georgia Tech face database, ORL face database, CVL face database, AR face database, and CASIA face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than the LRC, SRC, collaborative representation-based classification, regularized robust coding, relaxed collaborative representation, support vector machine, and TPTSSR for face recognition under various conditions.

In this paper, a new classifier, called multiple linear regression coefficients (MLRC), is proposed for image recognition. Linear regression classification (LRC) uses the linear combination of the class-model for classification. Sparse representation based classification (SRC) utilizes the globalmodel for classification. Based on the global-concept of SRC, mean representation classification (MRC) is proposed, which uses the mean vector of each class to constitute the global-model for classification. Motivated by LRC, MRC and SRC, this paper focuses on finding the better global-model for classification. MLRC firstly constitutes regression coefficient matrix and multiple sub-global-models. Afterwards, MLRC computes the weighted value with the coefficient matrix and solve the least square error with multiple sub-global-models for classification. The PolyU Finger-Knuckle-Print (FKP) and Multispectral palm-print (MSP) databases are used to assess the proposed classifier. Experimental results demonstrate that the proposed approach achieves a better recognition rate than the LRC, MRC, SRC and some state-of-the-art methods.

Sparse representation based classification has attracted much attention due to its robustness in the fields of biometrics, such as face recognition, palm-print recognition. SRC first constructs the linear representation model for the test sample, and then it classifies the test sample by comparing its coding error of each class. The coding error essentially can be viewed as the distance from sample to the subspace spanned by the samples from specific class. Therefore, the key to improve the classification performance of SRC is to enhance the subspace separability of these subspaces. In this paper, we introduce the difference subspace analysis into SRC, and propose difference subspace based SRC (DSSRC) for face recognition. Different from traditional dictionary learning based SRC methods, DSSRC focuses on maximizing the discriminability for the classes rather than the representation ability for the samples. Extensive experiments on the well-known image datasets demonstrate that the proposed DSSRC method is effective for face recognition.

This paper proposes the pairwise linear regression classification (PLRC) for image set retrieval. In PLRC, we first define a new concept of the unrelated subspace and introduce two strategies to constitute the unrelated subspace. In order to increase the information of maximizing the query set and the unrelated image set, we introduce a combination metric for two new classifiers based on two constitution strategies of the unrelated subspace. Extensive experiments on six well-known databases prove that the performance of PLRC is better than that of DLRC and several state-of-theart classifiers for different vision recognition tasks: clusterbased face recognition, video-based face recognition, object recognition and action recognition.

Traditional linear regression classification (LRC) suffers from a small sample size problem that the limited training samples of each class cannot comprehensively reflect different variations of the class. To address the problem, this paper proposes a novel iterative linear regression classification (ILRC) for image recognition. Different from traditional LRC, ILRC not only generates several new subspaces in each iteration but also uses the discrimination idea to optimize the training-set and testing samples. Extensive experiments on five benchmark databases demonstrate that the proposed ILRC classifier achieves better recognition performance than the traditional LRC and several state-of-the-art methods.

Recently, the linear regression classification (LRC) has been proposed. LRC uses linear regression representation of each class for classification. Based on LRC, a few improved methods are proposed, such as kernel linear regression classification (KLRC), kernel ridge regression classification (KRRC) and nearest regularized subspace (NRS). Motivated by the LRC, KLRC, KRRC and NRS, center-based weighted kernel linear regression (CWKLR) is proposed for image classification in this paper. CWKLR firstly constitutes the center-based kernel matrix based on the KLRC and KRRC. Next, CWKLR utilizes the Tikhonov Matrix to solve the weighted projection coefficients for classification. Experiments on Coil100 object database, Eth80 object database and GT face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than some state-of-the-art methods.

In this paper, two improved classifiers based on the nearest feature line (NFL) are proposed for image classification, where the neighborhood feature line segment-I (NFLS-I) classifier uses the neighborhood of prototypes to select the better-fitted feature lines (FLs) and the neighborhood feature line segment-II (NFLS-II) classifier utilizes the neighborhood of the query sample to choose more-likely FLs. With better selection of FLs, these two classifiers can both improve the recognition performance and the computation problem. A large number of experiments on Soil-100 object database, Yale face database, FEI face database, AR face database, and Jochen triesch static hand posture database are performed to evaluate these two proposed classifiers. The experimental results demonstrate that the proposed NFLS-I and NFLS-II classifiers outperform the original NFL and some other improved NFL classifiers for object recognition, hand posture recognition, and face recognition.

In this paper, two improved methods based on nearest feature plane (NFP), called as center-based nearest feature plane (CNFP) and line-based nearest feature plane (LNFP), are proposed for recognition. Borrowing the concept from the nearest neighbor plane (NNP) classifier and center-based nearest neighbor (CNN) classifier, the proposed methods choose the valuable representation of the class to reduce the computational complexity of NFP. At the same time, CNFP and LNFP try their best to get the better performance than NFP classifier. A large number of experiments on Yale face database and soil object database are used to evaluate the proposed algorithms. The experimental result demonstrate that the proposed method take lower computational complexity and achieve better recognition rate than the other improved classifiers.