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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Yang, Jian
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
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| 264 |
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|c 2017
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 06.08.2018
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| 500 |
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|a Date Revised 06.08.2018
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| 500 |
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|a published: Print-Electronic
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| 500 |
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Recently, regression analysis has become a popular tool for face recognition. Most existing regression methods use the one-dimensional, pixel-based error model, which characterizes the representation error individually, pixel by pixel, and thus neglects the two-dimensional structure of the error image. We observe that occlusion and illumination changes generally lead, approximately, to a low-rank error image. In order to make use of this low-rank structural information, this paper presents a two-dimensional image-matrix-based error model, namely, nuclear norm based matrix regression (NMR), for face representation and classification. NMR uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers (ADMM) to calculate the regression coefficients. We further develop a fast ADMM algorithm to solve the approximate NMR model and show it has a quadratic rate of convergence. We experiment using five popular face image databases: the Extended Yale B, AR, EURECOM, Multi-PIE and FRGC. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression-based methods for face recognition in the presence of occlusion and illumination variations
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Luo, Lei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Qian, Jianjun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tai, Ying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Fanlong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Yong
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 1 vom: 01. Jan., Seite 156-171
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:39
|g year:2017
|g number:1
|g day:01
|g month:01
|g pages:156-171
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