Fast and robust face recognition via coding residual map learning based adaptive masking

Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustne...

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Vydáno v:	Pattern recognition Ročník 47; číslo 2; s. 535 - 543
Hlavní autoři:	Yang, Meng, Feng, Zhizhao, Shiu, Simon C.K., Zhang, Lei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.02.2014 Elsevier
Témata:	Adaptive masking Applied sciences Artificial intelligence Coding Coding residual map Coding, codes Complexity Computer science; control theory; systems Exact sciences and technology Image processing Information, signal and communications theory Learning Masking Occlusion Pattern recognition Pattern recognition. Digital image processing. Computational geometry Pixels Representations Robust face recognition Signal and communications theory Signal processing Telecommunications and information theory Training Adaptive masking Robust face recognition Coding residual map Biometrics Computer vision Dictionaries Face recognition Image processing Threshold detection Pattern recognition Learning Accuracy Coding Residual impurity Database Masking Robustness Automatic recognition Time complexity
ISSN:	0031-3203, 1873-5142
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Abstract	Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustness to face occlusion; however, the high complexity of l1-minimization makes them less useful in practical applications. In this paper we propose a novel coding residual map learning scheme for fast and robust FR based on the fact that occluded pixels usually have higher coding residuals when representing an occluded face image over the non-occluded training samples. A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. Finally the face image is identified by masking the detected occlusion pixels from face representation. Experiments on benchmark databases show that the proposed scheme has much lower time complexity but comparable FR accuracy with other popular approaches. •A fast and robust face recognition method is proposed.•A residual map is learned to effectively enhance the robustness to occlusion.•The proposed method is much faster than RSC with slightly lower accuracy.
AbstractList	Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustness to face occlusion; however, the high complexity of l1-minimization makes them less useful in practical applications. In this paper we propose a novel coding residual map learning scheme for fast and robust FR based on the fact that occluded pixels usually have higher coding residuals when representing an occluded face image over the non-occluded training samples. A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. Finally the face image is identified by masking the detected occlusion pixels from face representation. Experiments on benchmark databases show that the proposed scheme has much lower time complexity but comparable FR accuracy with other popular approaches. Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustness to face occlusion; however, the high complexity of l1-minimization makes them less useful in practical applications. In this paper we propose a novel coding residual map learning scheme for fast and robust FR based on the fact that occluded pixels usually have higher coding residuals when representing an occluded face image over the non-occluded training samples. A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. Finally the face image is identified by masking the detected occlusion pixels from face representation. Experiments on benchmark databases show that the proposed scheme has much lower time complexity but comparable FR accuracy with other popular approaches. •A fast and robust face recognition method is proposed.•A residual map is learned to effectively enhance the robustness to occlusion.•The proposed method is much faster than RSC with slightly lower accuracy.
Author	Yang, Meng Zhang, Lei Feng, Zhizhao Shiu, Simon C.K.
Author_xml	– sequence: 1 givenname: Meng surname: Yang fullname: Yang, Meng – sequence: 2 givenname: Zhizhao surname: Feng fullname: Feng, Zhizhao – sequence: 3 givenname: Simon C.K. surname: Shiu fullname: Shiu, Simon C.K. – sequence: 4 givenname: Lei surname: Zhang fullname: Zhang, Lei email: cslzhang@comp.polyu.edu.hk
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Keywords	Adaptive masking Robust face recognition Coding residual map Biometrics Computer vision Dictionaries Face recognition Image processing Threshold detection Pattern recognition Learning Accuracy Coding Residual impurity Database Masking Robustness Automatic recognition Time complexity
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Snippet	Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR....
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SubjectTerms	Adaptive masking Applied sciences Artificial intelligence Coding Coding residual map Coding, codes Complexity Computer science; control theory; systems Exact sciences and technology Image processing Information, signal and communications theory Learning Masking Occlusion Pattern recognition Pattern recognition. Digital image processing. Computational geometry Pixels Representations Robust face recognition Signal and communications theory Signal processing Telecommunications and information theory Training
Title	Fast and robust face recognition via coding residual map learning based adaptive masking
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