Abstract
This paper concentrates on the comparisons of systems that are used for the recognition of expressions generated by six upper face action units (AUs) by using Facial Action Coding System (FACS). Haar wavelet, Haar-Like and Gabor wavelet coefficients are compared, using Adaboost for feature selection. The binary classification results by using Support Vector Machines (SVM) for the upper face AUs have been observed to be better than the current results in the literature, for example 96.5% for AU2 and 97.6% for AU5. In multi-class classification case, the Error Correcting Output Coding (ECOC) has been applied. Although for a large number of classes, the results are not as accurate as the binary case, ECOC has the advantage of solving all problems simultaneously; and for large numbers of training samples and small number of classes, error rates are improved.
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References
Ekman, P., Friesen, W.V.: Pictures of Facial Affect. Consulting Psychologist Press, Palo Alto (1976)
Izard, C., Dougherty, L., Hembree, E.A.: A System for Identifying Affect Expressions by Holistic Judgements. Univ. Of Delaware (unpublished manuscript) (1983)
Bartlett, M.S., Hager, J., Ekman, P., Sejnowski, T.: Measuring Facial Expressions by Computer Image Analysis. J. Psychophysiology 36, 253–263 (1999)
Bartlett, M.S., Littlewort, G., Lainscsek, C., Fasel, I., Movellan, J.: Machine Learning Methods for Fully Automatic Recognition of Facial Expressions and Facial Actions. In: IEEE International Conference on Systems, Men and Cybernetics, Netherlands, pp. 592–597 (2004)
Ekman, P., Friesen, W.V.: Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologist Press, Palo Alto (1978)
Mase, K.: Recognition of Facial Expression from Optical Flow. IEICE Trans. E74(10), 3474–3483 (1991)
Yacoob, Y., Davis, L.S.: Recognizing Human Facial Expression from Long Image Sequences Using Optical Flow. IEEE Trans. Pattern Analysis and Machine Intelligence 18(6), 636–642 (1996)
Suwa, M., Sugie, N., Fujimora, K.A.: Preliminary Note on Pattern Recognition of Human Emotional Expression. In: Proc. International Joint Conf. Pattern Recognition, pp. 408–410 (1978)
Lanitis, A., Taylor, C., Cootes, T.: Automatic Interpretation and Coding of Face Images Using Flexible Models. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 743–756 (1997)
Zhang, Z.: Feature-Based Facial Expression Recognition: Sensitivity Analysis and Experiments with a Multilayer Perceptron. Int’l. J. Pattern Recognition and Artificial Intelligence 13(6), 893–911 (1999)
Whitehill, J., Omlin, C.W.: Haar Features for FACS AU Recognition. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (2006)
Donato, G., Bartlett, M.S., Hager, J., Ekman, P., Sejnowski, T.J.: Classifying Facial Actions. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(10), 974–988 (1999)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International J. of Computer Vision 57(2), 137–154 (2004)
Freund, Y., Schapire, R.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Computer and System Sciences 55, 119–139 (1997)
Shen, L., Bai, L., Fairhurst, M.: Gabor Wavelets and General Discriminant Analysis for Face Identification and Verification. Image Vision Computing 25(5), 553–563 (2007)
Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Filters. Pattern Recognition 24(12), 1167–1186 (1991)
Lee, C.J., Wang, S.D.: Fingerprint Feature Extraction Using Gabor Filters. Electronics Letters 35(4), 288–290 (1999)
Zhan, Y., Niu, D., Cao, P.: Facial Expression Recognition Based on Gabor Wavelet Transformation and Elastic Templates Matching. In: Third International Conference on Image and Graphics (ICIG 2004), pp. 254–257 (2004)
Dietterich, T.G., Bakiri, G.: Solving Multi-class Learning Problems via Error-Correcting Output Codes. J. Artificial Intelligence Research 2, 263–286 (1995)
Tian, Y., Kanade, T., Cohn, J.F.: Recognizing Action Units for Facial Expression Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 97–115 (2001)
Efron, B.: Bootstrap methods: Another Look at the Jackknife. The Annals of Statistics 7(1), 1–26 (1979)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. School of Information and Computer Science, University of California, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A Training Algorithm for Optimal Margin Classifiers. In: COLT 1992: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)
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Zor, C., Windeatt, T. (2009). Upper Facial Action Unit Recognition. In: Tistarelli, M., Nixon, M.S. (eds) Advances in Biometrics. ICB 2009. Lecture Notes in Computer Science, vol 5558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01793-3_25
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DOI: https://doi.org/10.1007/978-3-642-01793-3_25
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