The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Sparse representation or collaborative representation: Which helps face recognition?
Abstract
As a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature. However, is it really the l1-norm sparsity that improves the FR accuracy? This paper devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS). The extensive experiments clearly show that CRC_RLS has very competitive classification results, while it has significantly less complexity than SRC.
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Cites
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Cited by
Deeply learned face representations are sparse, selective, and robust
Sun, Y., Wang, X., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298907 · Cited by 939
Collaborative representation for hyperspectral anomaly detection
Li, W., & Du, Q. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2343955 · Cited by 758
A survey of depth and inertial sensor fusion for human action recognition
Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach
He, L., Liang, J., Li, H., & Sun, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00739 · Cited by 341
Fast $\ell_{1}$-Minimization Algorithms for Robust Face Recognition
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Metadata
- Authors
- Zhang, L., Yang, M., & Feng, X.
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 471-478
- OpenAlex
- W2132467081
- Cited by
- 1,952
- References
- 4
- FWCI
- 78.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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