Facial Expression Recognition using Neural Network with Regularized Back-propagation Algorithm
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10.5120/13388-1019 |
Ashish Kumar Dogra, Nikesh Bajaj and Harish Kumar Dogra. Article: Facial Expression Recognition using Neural Network with Regularized Back-propagation Algorithm. International Journal of Computer Applications 77(5):5-8, September 2013. Full text available. BibTeX
@article{key:article,
author = {Ashish Kumar Dogra and Nikesh Bajaj and Harish Kumar Dogra},
title = {Article: Facial Expression Recognition using Neural Network with Regularized Back-propagation Algorithm},
journal = {International Journal of Computer Applications},
year = {2013},
volume = {77},
number = {5},
pages = {5-8},
month = {September},
note = {Full text available}
}
Abstract
Since decades, in the field of face expression recognition, many researchers have been developing numerous new techniques. These developments are being fueled by numerous advances in computer vision. Such advancement in the field of computer vision holds a promise of reducing error rate in face expression recognition system. This paper proposes an automatic facial expression recognition system using neural network with regularized back-propagation algorithm. The Cohn-Kanade database [5],[8] have been used having six different types of expression. The database is highly imbalance so face detector using viola Jones [2] have been used to crop and balance the database. Once balanced, an neural network approach used, obtained training set accuracy 99. 32% and testing accuracy 91. 9595%. The paper has got very good result of individual expression like happy and surprise expression, testing accuracy up to 100%. The reason why neural network is used, it might someday be able to learn in a manner similar to humankind.
References
- J. Russell and J. Dols, the Psychology of Facial Expression. Cambridge, U. K. : Cambridge Univ. Press, 1997, ser. Studies in Emotion
- P. Viola and M. Jones, "Robust real-time face detection," Int. J. Comput. Vis. , vol. 57, no. 2, pp. 137–154, May 2004.
- Z. Zeng, M. Pantic, G. Roisman, and T. Huang, "A survey of affect recognition methods: Audio, visual, and spontaneous expressions," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 31, no. 1, pp. 39–58, Jan. 2009.
- M. Valstar, B. Jiang, M. Méhu, M. Pantic, and K. Scherer, "The ?rst facial expression recognition and analysis challenge," in Proc. IEEE Int. Conf. FG, 2011, pp. 921–926.
- Y. Tian, T. Kanade, and J. Cohn, "Facial expression analysis," in Handbook of Face Recognition. New York: Springer-Verlag, 2005, pp. 247–276.
- Kanade, T. , Cohn, J. , & Tian, Y. (2000). "Comprehensive database for facial expression analysis, Proceedings of the fourth IEEE International Conference on Automatic Face and Gesture Recognition(FG'00), Grenoble, France, 46-53.