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Graph-Preserving Sparse Nonnegative Matrix Factorization With Application to Facial Expression Recognition
Abstract
In this paper, a novel graph-preserving sparse nonnegative matrix factorization (GSNMF) algorithm is proposed for facial expression recognition. The GSNMF algorithm is derived from the original NMF algorithm by exploiting both sparse and graph-preserving properties. The latter may contain the class information of the samples. Therefore, GSNMF can be conducted as an unsupervised or a supervised dimension reduction method. A sparse representation of the facial images is obtained by minimizing the l(1)-norm of the basis images. Furthermore, according to the graph embedding theory, the neighborhood of the samples is preserved by retaining the graph structure in the mapped space. The GSNMF decomposition transforms the high-dimensional facial expression images into a locality-preserving subspace with sparse representation. To guarantee convergence, we use the projected gradient method to calculate the nonnegative solution of GSNMF. Experiments are conducted on the JAFFE database and the Cohn-Kanade database with unoccluded and partially occluded facial images. The results show that the GSNMF algorithm provides better facial representations and achieves higher recognition rates than nonnegative matrix factorization. Moreover, GSNMF is also more robust to partial occlusions than other tested methods.
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Cites
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 14,193
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,798
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Lee, D. D., & Seung, H. S. (2000) · Cited by 7,972
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Seung, D., & Lee, L.-W. (2001) · Cited by 4,753
Locality Preserving Projections
He, X., & Niyogi, P. (2003) · Cited by 4,128
Face recognition using Laplacianfaces
He, X., Yan, S., Hu, Y., Niyogi, P., & Zhang, H.-J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.55 · Cited by 3,268
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Research works printed but not linked: 6
- 0, MPLab GENKI Database
- bengio, 2003, Out-of-sample extensions for LLE, ISPMAP, MDS, Eigenmaps, and spectral clustering, Proc Adv Neural Inf Process Syst, 177
- 10.1037/0022-3514.37.11.2049 · DOI
- buciu, 2008, Organic Computing in the Series Understanding Complex Systems, 303
- ekman, 2008, Improving quality prediction accuracy of P. 563 for noise suppression, Proc 11th Int Workshop Acoust Echo Noise Control
- 10.1109/NNSP.2002.1030067 · DOI
Cited by
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Zhang, L., Verma, B., Tjondronegoro, D., & Chandran, V. (2018) · ACM Computing Surveys · doi:10.1145/3158369 · Cited by 1,338
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Liu, P., Han, S., Meng, Z., & Tong, Y. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.233 · Cited by 667
Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527
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Ghimire, D., & Lee, J. (2013) · Sensors · doi:10.3390/s130607714 · Cited by 303
Luo, X., Zhou, M., Li, S., Xia, Y., You, Z., Zhu, Q., & LEUNG, H. K. N. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2685521 · Cited by 242
Zhang, S., Li, Q., Liu, J., & Zhou, X. J. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr206 · Cited by 241
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Sun, F., Xu, M., Hu, X., & Jiang, X. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.01.103 · Cited by 56
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Liu, P., Zhou, J. T., Tsang, I. W.-H., Meng, Z., Han, S., & Tong, Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_11 · Cited by 55
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Liao, J., Lin, Y., Ma, T., He, S., Liu, X., & He, G. (2023) · Sensors · doi:10.3390/s23094204 · Cited by 57
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Ngo, Q. T., & Yoon, S. (2020) · Sensors · doi:10.3390/s20092639 · Cited by 52
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Kumar, S., Singh, S., & Kumar, J. (2018) · Wireless Personal Communications · doi:10.1007/s11277-018-5923-y · Cited by 46
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Vo, D. M., & Lê, T. H. (2016) · National Foundation for Science and Technology Development Conference on Information and Computer Science · doi:10.1109/nics.2016.7725672 · Cited by 22
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Semi-supervised nonnegative matrix factorization with label propagation and constraint propagation
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NASRI, M. A., Hmani, M. A., Mtibaa, A., Petrovska‐Delacrétaz, D., Slima, M. B., & Hamida, A. B. (2020) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip49331.2020.9231537 · Cited by 19
Intensity enhancement via GAN for multimodal face expression recognition
Yang, H., Zhu, K., Huang, D., Li, H., Wang, Y., & Chen, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.022 · Cited by 19
Metadata
- Authors
- Zhi, R., Flierl, M., Ruan, Q., & Kleijn, W. B.
- Affiliations
- Beijing Jiaotong University (CN)
- KTH Royal Institute of Technology (SE)
- Venue
- IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
- Published
- 2011-02
- Volume
- 41
- Issue
- 1
- Pages
- 38-52
- ISSN
- 1083-4419 (ISSN-L)
- 1941-0492
- OpenAlex
- W2142584058
- Cited by
- 260
- References
- 51
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 16 · 2023: 20 · 2022: 28 · 2021: 21 · 2020: 20 · 2019: 16 · 2018: 23 · 2017: 18 · 2016: 21 · 2015: 16 · 2014: 19 · 2013: 15 · 2012: 10
- FWCI
- 11.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- facial expression recognition, sparse representation, graph embedding, local structure preservation, dimensionality reduction, partial occlusion robustness, gradient projection, JAFFE database, Cohn-Kanade dataset
- MeSH terms
- Algorithms, Facial Expression, Female, Humans, Image Processing, Computer-Assisted, Male, Multivariate Analysis, Databases, Factual, Biometric Identification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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