Robust Face Recognition via Sparse Representation

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...

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Published in	IEEE transactions on pattern analysis and machine intelligence Vol. 31; no. 2; pp. 210 - 227
Main Authors	Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Yi Ma
Format	Journal Article
Language	English
Published	Los Alamitos, CA IEEE 01.02.2009 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects	Algorithms Applied sciences Artificial Intelligence Biometry - methods Classification Classification algorithms Cluster Analysis compressed sensing Computer science; control theory; systems ell^{1} > minimization Exact sciences and technology Face - anatomy & histology Face recognition Feature extraction Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image recognition Lighting Linear regression Mathematical models Object recognition Occlusion occlusion and corruption Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Recognition Representations Reproducibility of Results Robustness Sensitivity and Specificity Signal representations sparse representation Studies Subtraction Technique validation and outlier rejection Occlusion Feature evaluation and selection Outlier rejection Classifier design and evaluation Face and gesture recognition Spare representation compressed sensing Image processing Linear model Image projection feature extraction Facies Object oriented Database Illumination Sparse representation Robustness Pattern analysis 1-minimization occlusion and corruption Pattern extraction Multimodel Validation Computer vision Probabilistic approach Face recognition Linear regression Minimization Regression analysis Pattern recognition Object recognition Multiple regression Luminance validation and outlier rejection Regression model Frontal Artificial intelligence Occultation Principal component analysis Image classification
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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 C 1 -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.
AbstractList	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 [abstract truncated by publisher]. 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 C 1 -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. 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 l{1}-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. 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 C super(1)-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. 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 l{1}-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.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 l{1}-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. 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.
Author	Yang, A.Y. Ganesh, A. Yi Ma Wright, J. Sastry, S.S.
Author_xml	– sequence: 1 givenname: J. surname: Wright fullname: Wright, J. organization: Coordinated Sci. Lab., Univ. of Illinois at Urbana Champaign, Urbana, IL – sequence: 2 givenname: A.Y. surname: Yang fullname: Yang, A.Y. organization: Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Berkeley, CA – sequence: 3 givenname: A. surname: Ganesh fullname: Ganesh, A. organization: Coordinated Sci. Lab., Univ. of Illinois at Urbana Champaign, Urbana, IL – sequence: 4 givenname: S.S. surname: Sastry fullname: Sastry, S.S. organization: Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Berkeley, CA – sequence: 5 surname: Yi Ma fullname: Yi Ma organization: Coordinated Sci. Lab., Univ. of Illinois at Urbana Champaign, Urbana, IL
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Keywords	Occlusion Feature evaluation and selection Outlier rejection Classifier design and evaluation Face and gesture recognition Spare representation compressed sensing Image processing Linear model Image projection feature extraction Facies Object oriented Database Illumination Sparse representation Robustness Pattern analysis 1-minimization occlusion and corruption Pattern extraction Multimodel Validation Computer vision Probabilistic approach Face recognition Linear regression Minimization Regression analysis Pattern recognition Object recognition Multiple regression Luminance validation and outlier rejection Regression model Frontal Artificial intelligence Occultation Principal component analysis Image classification
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Snippet	We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and... Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces,...
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SubjectTerms	Algorithms Applied sciences Artificial Intelligence Biometry - methods Classification Classification algorithms Cluster Analysis compressed sensing Computer science; control theory; systems ell^{1}--minimization Exact sciences and technology Face - anatomy & histology Face recognition Feature extraction Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image recognition Lighting Linear regression Mathematical models Object recognition Occlusion occlusion and corruption Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Recognition Representations Reproducibility of Results Robustness Sensitivity and Specificity Signal representations sparse representation Studies Subtraction Technique validation and outlier rejection
Title	Robust Face Recognition via Sparse Representation
URI	https://ieeexplore.ieee.org/document/4483511 https://www.ncbi.nlm.nih.gov/pubmed/19110489 https://www.proquest.com/docview/857471579 https://www.proquest.com/docview/1671242448 https://www.proquest.com/docview/34455106 https://www.proquest.com/docview/66777593 https://www.proquest.com/docview/903641134
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