Abstract
An effective method for the automatic classification of facial expressions into emotional categories is presented. The system is able to classify the user facial expression in terms of the six Ekman’s universal emotions (plus the neutral one), giving a membership confidence value to each emotional category. The method is capable of analysing any subject, male or female of any age and ethnicity. The classification strategy is based on a combination (weighted majority voting) of the five most used classifiers. Another significant difference with other works is that human assessment is taken into account in the evaluation of the results. The information obtained from the users classification makes it possible to verify the validity of our results and to increase the performance of our method.
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References
Boukricha, H., Becker, C., Wachsmuth, I.: Simulating Empathy for the Virtual Human Max. In: 2nd International Workshop on Emotion and Computing in conj. with the German Conference on Artificial Intelligence, Osnabrück, Germany, pp. 22–27 (2007)
Pantic, M., Rothkrantz, L.J.M.: Automatic Analysis of Facial Expressions: The State of the Art. Pattern Analysis and Machine Intelligence, IEEE Transactions 22(12), 1424–1445 (2000)
Zhang, Z., Lyons, M., Schuster, M., Akamatsu, S.: Comparison between Geometry-Based and Gabor Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron. In: Proc. Int’l Conf. Automatic Face and Gesture Recognition, pp. 454–459 (1998)
Wallace, M., Raouzaiou, A., Tsapatsoulis, N., Kollias, S.: Facial Expression Classification Based on MPEG-4 FAPs: The Use of Evidence and Prior Knowledge for Uncetainty Removal. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), vol. 1, pp. 51–54 (2004)
Hammal, Z., Couvreur, L., Caplier, A., Rombaut, M.: Facial Expressions Recognition Based on the Belief Theory: Comparison with Diferent Classifiers. In: Proc. 13th International Conference on Image Analysis and Processing (2005)
Pantic, M., Rothkrantz, L.J.M.: Expert System for Automatic Analysis of Facial Expression. Image and Vision Computing J. 18(11), 881–905 (2000)
Cerezo, E., Hupont, I.: Emotional Facial Expression Classification for Multimodal Interfaces. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 405–413. Springer, Heidelberg (2006)
Datcu, D., Rothkrantz, L.J.M.: Facial Expression Recognition in still pictures and videos using Active Appearance Models. A comparison approach. In: Proceedings of the 2007 International Conference on Computer Systems and Technologies, vol. 285(112) (2007)
Qinzhen, X., Pinzheng, Z., Wenjiang, P., Luxi, Y., Zhenya, H.: A Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 309–312 (2006)
Limin, M., Chelberg, D., Celenk, M.: Spatio-temporal modeling of facial expressions using Gabor-wavelets and hierarchical hidden Markov models. In: IEEE International Conference on Image Processing, vol. 2, pp. 57–60 (2005)
Cohen, I., Sebe, N., Garg, A., Chen, L.S., Huang, T.S.: Facial expression recognition from video sequences: temporal and static modeling. Computer Vision and Image Understanding 11(1-2), 160–187 (2003)
Duang-Duang, Y., Lian-Wen, J., Jun-Xun, Y., Li-Xin, Z., Jian-Cheng, H.: Facial expression recognition with Pyramid Gabor Features and Complete Kernel Fisher Linear Discriminant Analysis. International Journal of Information Technology 11(9), 91–100 (2005)
Edwards, G.J., Cootes, T.F., Taylor, C.J.: Face Recognition Using Active Appearance Models. In: Proc. European Conf. Computer Vision, vol. 2, pp. 581–695 (1998)
Ekman, P.: Facial Expression, the Handbook of Cognition and Emotion. John Wiley et Sons, Chichester (1999)
Esau, N., Wetzel, E., Kleinjohann, L., Keinjohann, B.: Real-time facial expression recognition using a fuzzy emotion model. In: IEEE International Fuzzy Systems Conference FUZZ-IEEE 2007, pp. 1–6 (2007)
Manresa-Yee, C., Varona, J., Perales, F.J.: Towards hands-free interfaces based on real-time robust facial gesture recognition. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 504–513. Springer, Heidelberg (2006)
FG-Net Database, http://www.mmk.ei.tum.de/~waf/fgnet/feedtum.html
Pantic, M., Valstar, M.F., Rademaker, R., Maat, L.: Web-based Database for Facial Expression Analysis. In: Proc. IEEE Int’l Conf. Multmedia and Expo. (2005)
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)
Bassili, J.N.: Emotion recognition: The role of facial movement and the relative importance of upper and lower areas of the face. Journal of Personality and Social Psychology 37, 2049–2059 (1997)
Cerezo, E., Baldassarri, S., Seron, F.: Interactive agents for multimodal emotional user interaction. In: IADIS Multi Conferences on Computer Science and Information Systems 2007, pp. 35–42 (2007)
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Hupont, I., Baldassarri, S., Del Hoyo, R., Cerezo, E. (2008). Effective Emotional Classification Combining Facial Classifiers and User Assessment. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2008. Lecture Notes in Computer Science, vol 5098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70517-8_42
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DOI: https://doi.org/10.1007/978-3-540-70517-8_42
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