Learning of Facial Gestures Using SVMs
Conference paper
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Abstract
This paper describes the implementation of a fast and accurate gesture recognition system. Image sequences are used to train a standard SVM to recognize Yes, No, and Neutral gestures from different users. We show that our system is able to detect facial gestures with more than 80% accuracy from even small input images.
Keywords
Facial Recognition SVM Machine LearningPreview
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References
- 1.Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. Computer, 1–30 (2001)Google Scholar
- 2.Hagg, J., Rkl, B., Akan, B., Asplund, L.: Gesture recognition using evolution strategy neural network, pp. 245–248. IEEE, Los Alamitos (2008)Google Scholar
- 3.Hasanuzzaman, M., Ampornaramveth, V., Bhuiyan, M.A., Shirai, Y., Ueno, H.: Real-time vision-based gesture recognition for human robot interaction. In: 2004 IEEE International Conference on Robotics and Biomimetics, pp. 413–418 (2004)Google Scholar
- 4.Lee, S.-W.: Automatic gesture recognition for intelligent human-robot interaction. In: 7th International Conference on Automatic Face and Gesture Recognition FGR 2006, pp. 645–650 (2006)Google Scholar
- 5.Mitra, S., Acharya, T.: Gesture recognition: A survey. IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews 37(3), 311–324 (2007)CrossRefGoogle Scholar
- 6.Oshita, M., Matsunaga, T.: Automatic learning of gesture recognition model using som and svm. Advances in Visual Computing, 751–759 (2010)Google Scholar
- 7.Valibeik, S., Yang, G.-Z.: Segmentation and Tracking for Vision Based Human Robot Interaction. IEEE, Los Alamitos (2008)CrossRefGoogle Scholar
- 8.Vapnik, V.N.: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5), 988–999 (1999)CrossRefGoogle Scholar
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