Facial Expression Recognition Based on Features Fusion

Abstract:

In this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features are obtained by making use of singular value decomposition (SVD). At the same time, the local features are obtained by utilizing principal component analysis (PCA) to extract the principal Gabor features. Finally, the feature vectors which are fused with global and local features are used to train SVM to realize the face expression recognition, and the computer simulation illustrates the effectivity of this method on the JAFFE database.

• Periodical: Applied Mechanics and Materials (Volumes 20-23)
• Pages: 1253-1259
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.20-23.1253
• Online since: January 2010
• Authors: Chang Jun Zhou, Xiao Peng Wei, Qiang Zhang
• Keywords: Face Expression Recognition, Fusion, Gabor, Principal Component Analysis (PCA), SVD

References

[1] A.K. Jain, S.Z. Li, Handbook of Face Recognition, 2005, Springer, New York, USA.

[2] R. Cowie, E. Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fedlena, and J.G. Taylor, Emotion Recognition in Human-Computer Interaction, IEEE Signal Processing, 2001, 18(1): 32-80.

DOI: 10.1109/79.911197

[3] J. Carroll, J. Russell, Facial expression in hollywood's portrayal of emotion. Journal of Personality and Social Psychology, 1997, 72: 164-176.

[4] L. Chen, Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction. PhD thesis, University of Illinois at Urbana- Champaign, Department of Electrical Engineering, (2000).

[5] Z. Hong, Algebraic feature extraction of image for recognition. Pattern Recognition, 1991, 24 (3): 211- 219.

DOI: 10.1016/0031-3203(91)90063-b

[6] M.A. Turk, A.P. Pentland, Eigenfaces for recognition. Journal of Cognitive Neuroscience . 1991, 3(1): 71-86.

[7] M. Gargesha, P. Kuchi Facial Expression recognition Using a Neural Network, Artificial Neural Computation Systems, (2002).

[8] C. Cortes and V. Vapnik, Support vector networks. Machine Learning, 1995, 20: 1-25.

[9] I. Cohen, N. Sebe, F. Cozman, M. Cirelo, T. Huang, Coding, analysis, interpretation, and recognition of facial expressions. Journal of Computer Vision and Image Understanding Special Issue on Face Recognition, (2003).

[10] L. Wang, Y.P. Li, A novel 2D Gabor wavelets window method for face recognition, in: Lecture Notes in Computer Science, 2006, 4105: 497-504.

DOI: 10.1007/11848035_66

[11] N. Cristianini, J. S. Taylor, An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2004, 82-108.

DOI: 10.1017/cbo9780511801389

[12] Y.Q. Xu, B.C. Li, B. Wang, Face Recognition by Fast Independent Component Analysis and Genetic Algorithm. Proceedings of the Fourth International Conference on Computer and Information Technology, (2004).

DOI: 10.1109/cit.2004.1357196