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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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-2
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2013.08.003