Facial expression recognition based on graph-preserving sparse non-negative matrix factorization

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2009, Pages 3293-3296

  Zhi, Ruicong ^a   Flierl, Markus ^b   Ruan, Qiuqi ^a   Kleijn, Bastiaan ^b  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Facial expression recognition; Graph preserving constraint; Non negative matrix factorization; Sparse representations

Indexed keywords

BLIND SOURCE SEPARATION; FACE RECOGNITION; GESTURE RECOGNITION; IMAGING SYSTEMS; LIGHT MEASUREMENT; MILITARY PHOTOGRAPHY;

CLASS INFORMATION; COEFFICIENT MATRIX; DISCRIMINANT POWER; FACIAL EXPRESSION RECOGNITION; FACIAL EXPRESSIONS; FACIAL IMAGES; IMAGE MATRIX; NON-NEGATIVE FACTORIZATION; NON-NEGATIVE MATRIX; NON-NEGATIVE MATRIX FACTORIZATION ALGORITHMS; NONNEGATIVE MATRIX FACTORIZATION; NOVEL ALGORITHM; RECOGNITION RATES; SPARSE NON-NEGATIVE MATRIX FACTORIZATIONS; SPARSE REPRESENTATION; SUB-SPACES;

FACTORIZATION;

EID: 77951956531     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2009.5413940     Document Type: Conference Paper

References (9)

1. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, "Face recognition using Laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, 2005.
2. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, 1999. (Pubitemid 129515838)
3. P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," Journal ofMachine Learning Research, vol. 5, pp. 1457-1469, 2004.
4. S. Z. Li, X.W. Hou, H. J. Zhang and Q. S. Cheng, "Learning spatially localized, parts-based representation," in Proceeding of the IEEE International Conference on Computer Vision and Pattern Recognition, Hawaii, USA, pp. 207-212, 2001.
5. C. J. Lin, "Projected gradient methods for non-negative matrix factorization," Neural Computation, vol. 17, no. 10, pp. 2756-2779, 2005.
6. D. Donoho, "For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution," Communications On Pure and Applied Mathematics, vol. 59, no. 6, pp. 797-829, 2006. (Pubitemid 43667226)
7. T. Kanade, J. Cohn, and Y. Tian, "Comprehensive database for facial expression analysis," in Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 46-53, 2000.
8. S. Zafeiriou, A. Tefas, I. Buciu, and I. Pitas, "Exploiting discriminant information in nonnegative matrix factorization with application", IEEE Transactions on Neural Networks, vol. 17, no. 3, pp. 683-695, 2006.
9. P. O. Hoyer, Non-negative sparse coding, http://www.cs.helsinki.fi/u/ phoyer/software.html