Facial Expression Recognition by Sparse Reconstruction with Robust Features
Facial expression analysis relies on the accurate detection of a few subtle face traces. According to specialists , facial expressions can be decomposed into a set of small Action Units (AU) corresponding to different face regions. In this paper, we propose to detect facial expressions with sparse reconstruction methods. Inspired by sparse regularization and sparse over-complete dictionaries, we aim at finding the minimal set of face atoms that can represent a given expression. l1 based reconstruction computes the deviation from the average face as an additive model of facial expression atoms and classify unknown expressions accordingly. We compared the proposed approach to existing methods on the well-known Cohn-Kanade (CK+) dataset . Results indicate that sparse reconstruction with l1 penalty outperforms SVM and k-NN baselines with the tested features. The best accuracy (97%) was obtained using sparse reconstruction in an unsupervised setting.
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- 1.Asthana, A., et al.: Evaluating AAM fitting methods for facial expression recognition. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–8. IEEE (2009)Google Scholar
- 2.Dahmane, M., Meunier, J.: Continuous emotion recognition using Gabor energy filters. In: Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, pp. 351–358 (2011)Google Scholar
- 3.Ekman, P., et al.: Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto (1978)Google Scholar
- 5.Littlewort, G., Fasel, I.: Fully automatic coding of basic expressions from video. INC MPLab Technical Report 6 (2002)Google Scholar
- 6.Lucey, P., et al.: The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp. 94–101. IEEE (2010)Google Scholar
- 7.Lyons, M., et al.: Coding facial expressions with Gabor wavelets. In: Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205. IEEE Comput. Soc. (1998)Google Scholar
- 9.Nagesh, P.: A compressive sensing approach for expression-invariant face recognition. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1518–1525. IEEE (2009)Google Scholar
- 10.Shan, C., et al.: Robust facial expression recognition using local binary patterns. In: IEEE International Conference on Image Processing 2005, pp. II–370. IEEE (2005)Google Scholar
- 11.Valstar, M.F., et al.: Meta-Analysis of the First Facial Expression Recognition Challenge. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 42 (2012)Google Scholar