Abstract
Robust lip contour detection plays an important role in Facial Expression Recognition (FER). However, the large variations emerged from different speakers, intensity conditions, poor texture of lips, weak contrast between lip and skin, high deformability of lip, beard, moustache, wrinkle, etc. often hamper the lip contour detection accuracy. The novelty of this research effort is that we propose a new lip boundary localization scheme using Game Theory (GT) to elicit lip contour accurately from a facial image. Furthermore, we apply a feature subset selection scheme based on Particle Swarm Optimization (PSO) to select the optimal facial features. We have conducted several sets of experiments to evaluate the proposed approach. The results show that the proposed approach has achieved recognition rates of 93.0% and 92.3% on the JAFFE and CK+ datasets, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Raghavendra, R., Dorizzi, B., Rao, A., Hemantha, G.: PSO versus AdaBoost for feature selection in multimodal biometrics. In: Proc. IEEE Intl. Conf. on Biometrics: Theory, Appl., and Syst., pp. 1–7 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Intl. Conf. on Neural Networks, pp. 1942–1948 (1995)
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)
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)
Vapnik, V.: Statistical Learning Theory. John Wiley and Sons, New York (1998)
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 and Particle Swarm Optimization. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-33564-8_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33563-1
Online ISBN: 978-3-642-33564-8
eBook Packages: Computer ScienceComputer Science (R0)