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Collaborative representation-based classification has shown promising results on cognitive vision tasks like face recognition. It solves a linear problem with l 1 or l 2 norm regularization to obtain a stable sparse representation. Previous studies showed that the collaboration representation assisted the output of optimum sparsity constraint, but the choice of regularization also played a crucial role in stable representation. In this paper, we proposed a novel discriminative collaborative representation-based classification method via regularization implemented by truncated total least squares algorithm. The key idea of the proposed method is combining two coefficients obtained by l 2 regularization and truncated TLS-based regularization. After evaluated by extensive experiments conducted on several benchmark facial databases, the proposed method is demonstrated to outperform the naive collaborative representation-based method, as well as some other state-of-the-art methods for face recognition. The regularization by truncation effectively and dramatically enhances sparsity constraint on coding coefficients in collaborative representation and increases robustness for face recognition. [ABSTRACT FROM AUTHOR]

Sparse representation based classification (SRC) and collaborative representation based classification (CRC) are two well-known methods in representation-based classification for face recognition. SRC emphasizes on the role of sparsity in coding, while CRC obtains an enhanced sparse representation with collaborative contributions from all classes. Previous studies show that sparsity of representation coefficients of SRC and CRC is significant for robust classification that may amplify the nonlocal contribution from other classes. However, the parameters are closely associated with the sparsity and it is very difficult to solve the optimal solutions. This paper proposed a novel representation-based image classification method that integrates SRC with CRC by a multiplication operation on the solutions to obtain enhanced sparse representation. We conducted experiments on several benchmark face databases and the experimental results showed that our algorithm produces higher accuracy on classification than both SRC and CRC. [ABSTRACT FROM AUTHOR]

Sparse representation methods based on l 1 and/or l 2 regularization have shown promising performance in different applications. Previous studies show that the l 1 regularization based representation has more sparse property, while the l 2 regularization based representation is much simpler and faster. However, when dealing with noisy data, both naive l 1 and l 2 regularization suffer from the issue of unsatisfactory robustness. In this paper, we explore the method to implement an antinoise sparse representation method for robust face recognition based on a joint version of l 1 and l 2 regularization. The contributions of this paper are mainly shown in the following aspects. First, a novel objective function combining both l 1 and l 2 regularization is proposed to implement an antinoise sparse representation. An iterative fitting operation via l 1 regularization is integrated with l 2 norm minimization, to obtain an antinoise classification. Second, the rationale how the proposed method produces promising discriminative and antinoise performance for face recognition is analyzed. The l 2 regularization enhances robustness and runs fast, and l 1 regularization helps cope with the noisy data. Third, the classification robustness of the proposed method is demonstrated by extensive experiments on several benchmark facial datasets. The method can be considered as an option for the expert systems for biometrics and other recognition problems facing unstable and noisy data. [ABSTRACT FROM AUTHOR]

• Propose the WELRMC method. • Propose the TPWELRMC method. • Design weighted sparsity augmented collaborative representation. • Design coarse to fine augmented representation. Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods. [ABSTRACT FROM AUTHOR]

Face recognition in real-world video surveillance needs to deal with a lot of challenges including low resolution, illumination variations, pose changes, occlusions and so on. Among them, occlusions are difficult and have not attracted enough attentions. To address this problem, in this paper, we propose a robust discriminative nonnegative dictionary learning method for occluded face recognition, which estimates the occlusions adaptively and selects the features robustly. Instead of modeling the reconstruction errors using a specific distribution, we estimate occlusions adaptively according to the reconstruction errors and learn different weights for different pixels during the iterative processing. To enhance discriminant ability of the dictionary, we constrain the low-dimensional representations of samples from the same class to be as close as possible and select the discriminative features robustly via ℓ 2, 1 -norm. For the induced non-convex problem, we reformulate it into local convex optimization subproblem via utilizing the half-quadratic technique and propose new update rules. Extensive experiments are implemented on four benchmark datasets, and the experimental results demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Feature space-based face recognition method performs representation and classification for face images in the feature space instead of the original space. This representation-based method uses kernel function to solve the feature space and therefore outperforms the method working in original space. A contemporary coarse-to-fine face recognition method also uses the idea of sparse representation but separates the classification into two stages from coarse candidate to fine classification. They both apply the sparse representation technique for image classification, but we believe that there is still space to improve the robustness of sparse representation in these methods. We proposed a novel method integrating both methods together to perform robust face image classification. This method also consists of two stages as sparse representation-based coarse-to-fine classification. In the first stage, however, the coarse candidate classes are determined in the feature space selected by the kernel function instead, which is the key technique used in the feature space-based method. And then in the next stage, fine classification is performed to assign the test sample into a class from candidate classes. The experimental results demonstrated that this novel integrated method outperforms both methods on prevailing benchmark facial datasets. [ABSTRACT FROM AUTHOR]