Noise-free representation based classification and face recognition experiments
TL;DR: This paper proposes a new representation based classification method that can effectively and simultaneously reduce noise in the test and training samples and then exploits them to determine the label of the test sample.
About: This article is published in Neurocomputing.The article was published on 2015-01-05. It has received 15 citations till now. The article focuses on the topics: One-class classification & Noise.
Citations
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01 Jan 2016
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197Â citations
TL;DR: A thorough and comprehensive comparative study in which different methods for face recognition are compared by conducting an in-depth analysis on them and demonstrates the significant advantage of combining the virtual sample generation technology with representation based methods.
Abstract: Despite considerable advances made in face recognition in recent years, the recognition performance still suffers from insufficient training samples. Hence, various algorithms have been proposed for addressing the problems of small sample size with dramatic variations in illuminations, poses and facial expressions in face recognition. Among these algorithms, the virtual sample generation technology achieves promising performance with reasonable and effective mathematical function and easy implementation. In this paper, we systematically summarize the research progress in the virtual sample generation technology for face recognition and categorize the existing methods into three groups, namely, (1) construction of virtual face images based on the face structure; (2) construction of virtual face images based on the idea of perturbation and distribution function of samples; (3) construction of virtual face images based on the sample viewpoint. We carry out thorough and comprehensive comparative study in which different methods are compared by conducting an in-depth analysis on them. It demonstrates the significant advantage of combining the virtual sample generation technology with representation based methods.
50Â citations
TL;DR: Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample.
Abstract: Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method ...
24Â citations
TL;DR: To improve the effectiveness of representation coding, an error detection machine (EDM) with multiple error detectors (ED) in SBRC, is proposed to detect and remove destroyed features (i.e. pixels) on a testing image.
22Â citations
25 May 2018
TL;DR: A new sparse representation-based classification method which can strengthen the discriminative property of different classes and obtain a better representation coefficient vector and experimental results show that the proposed method has better classification performance than other methods.
Abstract: In sparse representation algorithms, a test sample can be sufficiently represented by exploiting only the training samples from the same class. However, due to variations of facial expressions, illuminations and poses, the other classes also have different degrees of influence on the linear representation of the test sample. Therefore, in order to represent a test sample more accurately, we propose a new sparse representation-based classification method which can strengthen the discriminative property of different classes and obtain a better representation coefficient vector. In our method, we introduce a weighted matrix, which can make small deviations correspond to higher weights and large deviations correspond to lower weights. Meanwhile, we improve the constraint term of representation coefficients, which can enhance the distinctiveness of different classes and make a better positive contribution to classification. In addition, motivated by the work of ProCRC algorithm, we take into account the deviation between the linear combination of all training samples and of each class. Thereby, the discriminative representation of the test sample is further guaranteed. Experimental results on the ORL, FERET, Extended-YaleB and AR databases show that the proposed method has better classification performance than other methods.
19Â citations
Cites methods from "Noise-free representation based cla..."
...The classification accuracy rate of our method can outperform the CRC, KRBM, INNC, CFKNNC, NFRBC, SRC and PCRC algorithms by a margin of 9.20, 19.00, 8.50, 3.40, 3.20, 5.20 and 7.80%, respectively....
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...In addition, although our method is lower 12.67% than NFRBC when the number of training samples per class is five, with the increase of training samples, the classification accuracy of our method quickly surpassed that of NFRBC, and the rising range of our method is significantly higher than that of NFRBC....
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...These algorithms include CRC, INNC [52], CFKNNC [53], NFRBC [54], KRBM [55], SRC, LRC, PCRC and MI-SRC [56]....
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References
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TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Abstract: We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.
9,658Â citations
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.
6,384Â citations
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01 Jan 1998
3,650Â citations
"Noise-free representation based cla..." refers methods in this paper
...For the AR face database [50], the images of each subject have different facial expressions, and were acquired under lighting conditions and with and without occlusions....
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TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.
Abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
3,439Â citations
TL;DR: Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
Abstract: We propose an appearance-based face recognition method called the Laplacianface approach. By using locality preserving projections (LPP), the face images are mapped into a face subspace for analysis. Different from principal component analysis (PCA) and linear discriminant analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
3,314Â citations
"Noise-free representation based cla..." refers background in this paper
...For example, Laplacian faces [36], semisupervised multiview distance metric learning [37], elastic manifold embedding [38] and adaptive hypergraph learning [39] all address the problem of preserving locality structures of samples from different viewpoints....
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