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In real applications, the observed low-resolution face images usually have pose variations. Conventional learning-based methods ignore these variations; thus, the hallucinated high-resolution faces are not reasonable for the following recognition task. For recognition purpose, we prefer to obtain near-frontal faces. To this end, we propose a nuclear norm regularized structural orthogonal Procrustes regression (N2SOPR) approach in this work to acquire pose-robust feature representations for face hallucination with pose. The orthogonal Procrustes regression is used to seek an appropriate transformation between two data matrixes. Additionally, the nuclear norm regularization is imposed on the representation residual to preserve image structural property. We also impose a low-rank restraint on the combination weight to automatically cluster each input into the same subspace with the training samples. Both hallucination and recognition experiments conducted on common face databases have verified that our N2SOPR can obtain reasonable performance than some related methods.

Canonical correlation analysis (CCA) is a kind of classical multivariate analysis method. Less canonical correlation variables are used to describe the relationship between two variables completely but easily. To get high face recognition rate under low-resolution degradation over a long distance solidly, in this work, CCA is used to extract the correlation between high-resolution face images and low-resolution ones and to find the transform pair between them. Therefore, face images of the same individual with variable resolutions can be matched accurately. This is the first method that uses CCA to do low-resolution degradation face recognition over long distances. We conduct the experiments on the Extended Yale B and ORL database, and the experimental results validate the efficacy of the proposed method.

In the past several decades, classifier design has attracted much attention. Inspired by the locality preserving idea of manifold learning, here we give a local linear regression (LLR) classifier. The proposed classifier consists of three steps: first, search k nearest neighbors of a pointed sample from each special class, respectively; second, reconstruct the pointed sample using the k nearest neighbors from each special class, respectively; and third, classify the test sample according to the minimum reconstruction error. The experimental results on the ETH80 database, the CENPAMI handwritten number database and the FERET face image database demonstrate that LLR works well, leading to promising image classification performance.

Sample misalignment exerts an important influence on training a rapid and accurate human detector, and it is a difficult problem to tackle with due to human articulation or manual annotation errors. Multiple instances learning method is an effective tool to deal with this difficulty without manual correction. In this paper, firstly, we propose a variable granularity HOG-CSLBP feature, which combines the human shape information with local texture information, and encodes spatial relationship in different granularity to improve its discriminative ability. Our new feature takes an advantage of the mutual complementarities of histogram of gradient and center-symmetric local binary patterns feature, which is adept at modeling human. Secondly, we present a Gentle MILBoost algorithm which utilizes the Newton update technique to get an optimal weak classifier that is able to discriminate complex distribution and is more stable in numerical computation. Experimental results based on INRIA, MIT-CBCL and TUD-Brussels datasets have showed superior performance of our method. Moreover, our method can achieve real-time speed in real application.

As we know, classical Fisher discriminant analysis usually suffers from the small sample size problem due to the singularity problem of the within-class scatter matrix. In this paper, a novel fuzzy linear classifier, called fuzzy maximum scatter difference (FMSD) discriminant criterion, is proposed to extract features from samples, especially deals with outlier samples. FMSD takes the scatter difference between between-class and within-class as discriminant criterion, so it will not suffer from the small sample size problem. The conventional scatter difference discriminant criterion (SDDC) assumes the same level of relevance of each sample to the corresponding class. In this paper, the fuzzy set theory is introduced to the conventional SDDC algorithm, where the fuzzy k-nearest neighbor is adopted to achieve the distribution information of original samples. The distribution is utilized to redefine the scatter matrices that are different from the conventional SDDC and effective to extract discriminative features from outlier samples. Experiments conducted on FERET and ORL face databases demonstrate the effectiveness of the proposed method.

For better representation and feature extraction of face images, a two-stage method, called Sequential Row–Column 2DPCA (RC2DPCA), is proposed in this paper, which uses 2DPCA operated in the row direction and alternative 2DPCA operated in column direction. RC2DPCA can compress image in row and column direction. RC2DPCA needs fewer coefficients for image representation than 2DPCA. The experimental results on the ORL and FERET face database show that RC2DPCA works well and surpasses 2DPCA, BDPCA, and GPCA.