Abstract
This paper proposes a double-stage classification model for the classification of six basic facial expressions. Inspired for the fact that an increase in the number of classes brings a drop on the accuracy for facial expression recognition, we use classifiers with fewer classes to improve the performance of a six-class expression recognition classifier. Support Vector Machine (SVM) is adopted as the classifiers due to its excellent performance in small databases. To make SVMs classify samples more precisely, selecting more support vectors trains the model. Active Shape Model (ASM) is used to locate shape points. The shape points are used as features to train the double-stage SVM, which includes a six-class SVM and a following few-class SVM with the classes corresponding to the largest classification probabilities of the former. The approach in this paper achieves an accuracy of 98.25% on the Japanese Female Facial Expression (JAFFE) database, 3.08% and 5.53% higher than those of Local Curvelet Transform method Facial Movement Features method respectively, and besides far better than six other methods.
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References
Ekman, P., Friesen, W.V., Ellsworth, P.: Emotion in the Human Face: Guidelines for Research and an Integration of Findings. Pergamon Press, New York (1972)
Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recogn. 36, 259–275 (2003). doi:10.1016/S0031-3203(02)00052-3
Pantic, M., Rothkrantz, L.J.M.: Facial action recognition for facial expression analysis from static face images. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34, 1449–1461 (2004). doi:10.1109/TSMCB.2004.825931
Tu, Y.H., Hsu, C.T.: Dual subspace nonnegative matrix factorization for person-invariant facial expression recognition. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 2391–2394 (2012)
Zhang, L., Tjondronegoro, D.: Facial expression recognition using facial movement features. IEEE Trans. Affect. Comput. 2, 219–229 (2011). doi:10.1109/T-AFFC.2011.13
Saha, A., Wu, Q.M.J.: Facial expression recognition using curvelet based local binary patterns. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2470–2473 (2010). doi:10.1109/ICASSP.2010.5494892
Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998). doi:10.1109/AFGR.1998.670949
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995). doi:10.1006/cviu.1995.1004
Lin, H.-T., Lin, C.-J., Weng, R.C.: A note on Platt’s probabilistic outputs for support vector machines. Mach. Learn. 68, 267–276 (2007). doi:10.1007/s10994-007-5018-6
Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011). doi:10.1145/1961189.1961199
Wu, P., Li, X.H., Zhou, J.L., Lei, G.: Face expression recognition based on feature fusion. In: 2009 International Workshop on Intelligent Systems and Applications, pp. 1–4 (2009). doi:10.1109/IWISA.2009.5072861
Abdulrahman, M., Gwadabe, T.R., Abdu, F.J., Eleyan, A.: Gabor wavelet transform based facial expression recognition using PCA and LBP. In: 2014 22nd Signal Processing and Communications Applications Conference (SIU), pp. 2265–2268 (2014). doi:10.1109/SIU.2014.6830717
Yu, K., Wang, Z., Zhuo, L., Feng, D.: Harvesting web images for realistic facial expression recognition. In: 2010 International Conference on Digital Image Computing: Techniques and Applications, pp. 516–521 (2010). doi:10.1109/DICTA.2010.93
Uçar, A., Demir, Y., Güzeliş, C.: A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering. Neural Comput. Appl. 27, 131–142 (2016). doi:10.1007/s00521-014-1569-1
Acknowledgments
This work was supported in part by National Natural Science Foundation of China under grant 61371148. The authors appreciate the suggestion of using SVM as classifiers in our research from both Linlu Wang and Zhaohui Meng.
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Yu, T., Gu, X. (2017). Facial Expression Recognition Using Double-Stage Sample-Selected SVM. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_28
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DOI: https://doi.org/10.1007/978-3-319-63309-1_28
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