The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
Open accessAbstract
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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Robust Face Recognition via Sparse Representation
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Robust principal component analysis?
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A Singular Value Thresholding Algorithm for Matrix Completion
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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
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Cited by
Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network
Song, L., Gong, D., Li, Z., Liu, C., & Liu, W. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00086 · Cited by 267
Metadata
- Authors
- Yang, J., Luo, L., Qian, J., Tai, Y., Zhang, F., & Xu, Y.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-01-01
- Volume
- 39
- Issue
- 1
- Pages
- 156-171
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1600550542
- Cited by
- 360
- References
- 60
- FWCI
- 33.54
- Type
- journal article
- Language
- en
- Open access
- green
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- Yang, J., Luo, L., Qian, J., Tai, Y., Zhang, F., & Xu, Y. (2017). Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2016.2535218
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