Abstract
Accurate detection of lip contour is important in many application areas, including biometric authentication, human computer interaction, and facial expression recognition. In this paper, we propose a new lip boundary localization scheme based on Game Theory (GT) to improve the facial expression detection performance. In addition, we use GT for selecting the proper set of facial features. We apply the Extended Contribution-Selection Algorithm (ECSA) for the dimensionality reduction of the facial features using a coalitional GT-based framework. We have conducted several sets of experiments to evaluate the proposed approach. The results show that the proposed approach has achieved recognition rates of 93.1% and 92.7% on the JAFFE and CK+ datasets, respectively.
Chapter PDF
Similar content being viewed by others
Keywords
References
Shan, C., Gong, S., McOwan, P.: Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vis. Comput. 27(6), 803–816 (2009)
Li, K., Wang, M., Liu, M., Zhao, A.: Improved level set method for lip contour detection. In: IEEE Intl. Conf. Image Process., pp. 673–676 (2010)
Chakraborty, A., Duncan, J.: Game-theoretic integration for image segmentation. IEEE Trans. Pattern Anal. and Machine Intell. 21(1), 12–30 (1999)
Makrehchi, M., Kamel, M.: Aggressive feature selection by feature ranking. In: Liu, H., Motoda, H. (eds.) Computational Methods of Feature Selection, pp. 313–330. Chapman and Hall/CRC Press (2007)
Cohen, S., Dror, D., Ruppin, E.: Feature selection via coalitional game theory. Neural Computa. 19, 1939–1961 (2007)
Viola, P., Jones, M.: Robust Real-Time Face Detection. Intl. J. Comp. Vis. 57(2), 137–154 (2004)
Ginneken, B., Frangi, A., Staal, J., Romeny, B., Viergever, M.: Active shape model segmentation with optimal features. IEEE Trans. Medical Imaging 21(8), 924–933 (2002)
Li, C., Xu, C., Gui, C., Fox, M.: Level set evolution without re-initialization: a new variational formulation. In: Proc. IEEE Intl. Conf. Comp. Vis. and Pattern Recog., pp. 430–436 (2005)
Bashyal, S., Venayagamoorthy, G.: Recognition of facial expressions using Gabor wavelets and learning vector quantization. Intl. J. Engg. App. of Artificial Intell. 21(7), 1–9 (2008)
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1998)
Lyons, M., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Anal. and Machine Intell. 21(12), 1357–1362 (1999)
Lucey, P., Cohn, J., Kanade, T., Saragih, J., Ambadar, Z.: The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression. In: IEEE Intl. Conf. Computer Vis. and Pattern Recog. Workshop, pp. 94–101 (2010)
Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Analysis and Machine Intelligence 22(3), 266–280 (2000)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models—their training and application. Computer Vis. Image Understand. 61(1), 38–59 (1995)
Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Professional (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roy, K., Kamel, M.S. (2012). Facial Expression Recognition Using Game Theory. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-33212-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33211-1
Online ISBN: 978-3-642-33212-8
eBook Packages: Computer ScienceComputer Science (R0)