Joint Spatial Geometric and Max-margin Classifier Constraints for Facial Expression Recognition Using Nonnegative Matrix Factorization
Keywords:
facial expressions; classification; nonnegative matrix factorization; graph regularization; spatial constraintsAbstract
Based on the constrained non-negative matrix factor algorithm, the article presents a new approach to facial recognition recognition. Our proposed method incorporated two tasks in an automatic expression analysis system: facial feature extraction and classification into expressions. To obtain local and geometric structure information in the data as much as possible, we amalgamate max-margin relegation into the constrained NMF optimization, resulting in a multiplicative updating algorithm is additionally proposed for solving optimization quandary. Experimental results on JAFFE dataset demonstrate that the effectiveness of the proposed method with improved performances over the conventional dimension reduction methods.
References
. B. Fasel, J. Luettin, “Automatic facial expression analysis: a survey”, Pattern Recognition, 36 (1) (2003), pp. 259-275.
. Ekman. P, Friesen W.V, "Constants across cultures in the face and emotion”, Journal of Personality and Social Psychology, 17: 124–129, 1971.
. L. Mai, “Joint Support Vector Machine with Constrained Nonnegative Matrix Factorization and Its Applications”, Master Thesis, National Central University, July 2017.
. Ying Zilu, Zhang Guoyi, "Facial Expression Recognition Based on NMF and SVM", IEEE: International Forum on Information Technology and Applications, pp. 612-615, 2009.
. H. Moon and P. J. Phillips, “Computational and performance aspects of PCAbased face-recognition algorithms.,” Perception, vol. 30, no. 3. pp. 303–21, Jan- 2001.
. H. Wechsler, “Enhanced Fisher linear discriminant models for face recognition,” Proceedings. Fourteenth Int. Conf. Pattern Recognit. (Cat. No.98EX170), vol. 2, pp. 1368–1372.
. L.-F. Chen, H.-Y. M. Liao, M.-T. Ko, J.-C. Lin, and G.-J. Yu, “A new LDA-based face recognition system which can solve the small sample size problem,” Pattern Recognit., vol. 33, no. 10, pp. 1713–1726, Oct. 2000.
. D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization”, Advances in Neural Information Processing System, 2000.
. W. S. Zheng, J. Lai, S. Liao, R. He, “Extracting non-negative basis images using pixel dispersion penalty”, Pattern Recognition, pp. 2912-2926, 2012.
. D. Cai, X. He, J. Han and T. Huang, “Graph regularized nonnegative matrix factorization for data representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33(8), pp. 1548–1560, 2011.
. B. G. Kumar, I. Kotsia and I. Patras, “Max-margin nonnegative matrix factorization”, Image Vision Computing, pp. 279–291, 2012.
. C. Ding, T. Li, and M. Jordan, “Convex and semi-nonnegative matrix factorizations for clustering and low-dimension representation”, Technical Report LBNL-60428, Lawrence Berkeley National Laboratory, University of California, Berkeley, 2006.
. Matthew N Dailey, Carrie Joyce, Michael J Lyons, Miyuki Kamachi, Hanae Ishi, Jiro Gyoba, & Garrison W Cottrell, “Evidence and a computational explanation of cultural differences in facial expression recognition”. Emotion, Vol 10(6), Dec 2010, 874-893.
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