Face Recognition Based on Circularly Symmetrical Gabor Transforms and Collaborative Representation

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.

Collaborative representation based classification (CRC) is an efficient classifier in image classification. By using regularization, the collaborative representation based classifier holds competitive performances compared with the sparse representation based classifier using less computational time. However, each of the elements calculated from the training samples are utilized for representation without selection, which can lead to poor performances in some classification tasks. To resolve this issue, in this paper, we propose a novel collaborative representation by directly using non-negative representations to represent a test sample collaboratively, termed Non-negative Collaborative Representation-based Classifier (NCRC). To collect all non-negative collaborative representations, we introduce a Rectified Linear Unit (ReLU) function to perform filtering on the coefficients obtained by minimization according to CRC’s objective function. Next, we represent the test sample by using a linear combination of these representations. Lastly, the nearest subspace classifier is used to perform classification on the test samples. The experiments performed on four different databases including face and palmprint showed the promising results of the proposed method. Accuracy comparisons with other state-of-art sparse representation-based classifiers demonstrated the effectiveness of NCRC at image classification. In addition, the proposed NCRC consumes less computational time, further illustrating the efficiency of NCRC.

During the last decade, sparse representations have been successfully applied to design high-performing classification algorithms such as the classical sparse representation based classification (SRC) algorithm. More recently, collaborative representation based classification (CRC) has emerged as a very powerful approach, especially for face recognition. CRC takes advantage of SRC through the notion of collaborative representation, relying on the observation that the collaborative property is more crucial for classification than the l1-norm sparsity constraint on coding coefficients used in SRC. This paper follows the same general philosophy of CRC and its main novelty is the application of a virtual collaborative projection (VCP) routine designed to train images of every class against the other classes to improve fidelity before the projection of the query image. We combine this routine with a method of local feature extraction based on high-order statistical moments to further improve the representation. We demonstrate using extensive experiments of face recognition and classification that our approach performs very competitively with respect to state-of-the-art classification methods. For instance, using the AR face dataset, our method reaches 100% of accuracy for dimensionality 300.

In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.

Mapping cotton cultivated area combining remote sensing with a fused representation-based classification algorithm

Representation-based classification have received much attention in the field of face recognition. Collaborative representation-based classification (CRC) has shown the robustness and high performance. In this paper, we proposed a new feature extraction method-based collaborative representation. Firstly, we get the coefficients of all face samples by collaborative representation. Then we define the inter-class reconstructive errors and intra-class reconstructive errors for each sample. After that, Fisher criterion is used to get the discriminative feature. At last, CRC is executed to get the identification results in the new feature space. Different from other feature extraction methods, the proposed method integrates the classification criterion into the feature extraction. So the feature space we get fits the classifier better. Experiment results on several face databases show that the proposed method is more effective than other state-of-the-art face recognition methods.

Although sparse representation based classification (SRC) has gained great success, doubts on the necessity of sparse constraint come in recent years. And collaborative representation based classification (CRC) has attracted much attention from researchers in fields of signal processing, image processing and pattern recognition. In this paper, an algorithm called collaborative dictionary learning with structured incoherence (CDLSI) is proposed for collaborative representation based detection (CRD), which can be viewed as a binary classification problem, in hyperspectral imagery (HSI). An inter-class incoherence term is added to make sub-dictionaries to be as independent as possible. During the optimizing procedure, sub-dictionaries are updated atoms-by-atoms with metaface method. Specifically, considering the non-sparse representation of CRC, the coefficients are iteratively optimized with l₂ -norm regularization during the coding procedure in CDLSI. Once the sub-dictionaries are obtained, the collaborative representation based technique is then used for detection. The proposed algorithm is applied to several real hyperspectral images for detection. Experimental results confirm the effectiveness of the proposed approach, and prove the superiority to the traditional algorithms.

We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional subspace learning methods and recently proposed sparse representation based classification (SRC) might be degraded when corrupted training samples are provided. In addition, sparsity based approaches are time-consuming due to the sparsity constraint. To alleviate the aforementioned problems to some extent, in this paper, we propose a discriminative low-rank representation method for collaborative representation-based (DLRR-CR) robust face recognition. DLRR-CR not only obtains a clean dictionary, it further forces the sub-dictionaries for distinct classes to be as independent as possible by introducing a structural incoherence regularization term. Simultaneously, a low-rank projection matrix can be learned to remove the possible corruptions in the testing samples. Collaborative representation based classification (CRC) method is exploited in our proposed method which has closed-form solution. Experimental results obtained on public face databases verify the effectiveness and robustness of our method.

The sparse representation-based classifier (SRC) has been developed and verified as having great potential for real-world face recognition. In this paper, we propose a discriminative projection and representation-based classification (DPRC) method to enhance the discriminant ability of the SRC. The proposed method first obtains a discriminative projection matrix not only maximizing the ratio of the distance within interclass over the distance within intraclass, but also minimizing the linear approximation error within intraclass. Then it maps the original data onto the discriminative space, and adopts an SRC method to obtain the final solution. An inexact augmented Lagrangian method of multiplier is proposed for finding the optimal representation vector in our framework, and a proximal alternating minimization method is adopted to the iteration subproblems of the proposed method. The proposed method is proven to have the subsequence convergence property. Experimental results on Yale, ORL, and AR face image databases demonstrate that, compared with some existing feature extraction methods based on the SRC, the proposed DPRC method is more efficient.

Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representation based classification (CRC) shows very effective face recognition performance with low computational cost. However, the recognition rate of CRC will drop dramatically when the available training samples per subject are very limited. One intuitive solution to this problem is operating CRC on patches and combining the recognition outputs of all patches. Nonetheless, the setting of patch size is a non-trivial task. Considering the fact that patches on different scales can have complementary information for classification, we propose a multi-scale patch based CRC method, while the ensemble of multi-scale outputs is achieved by regularized margin distribution optimization. Our extensive experiments validated that the proposed method outperforms many state-of-the-art patch based face recognition algorithms.

Robust automatic facial expression detection method based on sparse representation plus LBP map

The theory of compressive sensing applies the sparse representation to the extraction of useful information from signals and brings a breakthrough to the theory of signal sampling. Based on compressive sensing, sparse representation-based classification (SRC) is proposed. SRC uses the compressibility of the image data to represent the facial image sparsely and could solve the problems of both massive calculation and information loss in dealing with signals. SRC does not, however, deal with the effects of variable illumination, posture and incomplete face image, which could result in severe performance degradation. This paper studies the differences between SRC recognition and human recognition. We find that there is an obvious disadvantage in the SRC algorithm, and it will significantly affect the face recognition performance in actual environment, especially for the variable illumination, posture and incomplete face image. To overcome the disadvantage of SRC algorithm, we propose an SRC-based twice face recognition algorithm named T_SRC. T_SRC uses bidirectional PCA, linear discriminant analysis and GradientFace to execute multichannel analysis, which could extract more "holistic/configural" face features in actual environment than by using SRC algorithm directly. Based on the multichannel analysis, we identify the test image by SRC firstly. Then, by analyzing the residual, this algorithm could decide whether the twice recognition is needed. If the twice recognition is needed, T_SRC extracts the facial details ("featural" face features) by the improved Harris point and Gabor filter detector. We suppose that the facial details are more stable than the whole face in actual environment, and later experiments verify our assumption. At last, this algorithm identifies the class of the test image by SRC again. The results of the experiments prove that the T_SRC algorithm has better recognition rate than SRC.

Collaborative representation based face classification exploiting block weighted LBP and analysis dictionary learning

Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person

Collaborative representation methods, such as sparse subspace clustering (SSC) and sparse representation-based classification (SRC), have achieved great success in face clustering and classification by directly utilizing the training images as the dictionary bases. In this paper, we reveal that the superior performance of collaborative representation relies heavily on the sufficiently large class separability of the controlled face datasets such as Extended Yale B. On the uncontrolled or undersampled dataset, however, collaborative representation suffers from the misleading coefficients of the incorrect classes. To address this limitation, inspired by the success of linear discriminant analysis (LDA), we develop a superposed linear representation classifier (SLRC) to cast the recognition problem by representing the test image in term of a superposition of the class centroids and the shared intra-class differences. In spite of its simplicity and approximation, the SLRC largely improves the generalization ability of collaborative representation, and competes well with more sophisticated dictionary learning techniques, on the experiments of AR and FRGC databases. Enforced with the sparsity constraint, SLRC achieves the state-of-the-art performance on FERET database using single sample per person.