Abstract
Facial expression analysis has been well studied in recent years; however, these mainly focus on domains of posed or clear facial expressions. Meanwhile, subtle/micro-expressions are rarely analyzed, due to three main difficulties: inter-class similarity (hardly discriminate facial expressions of two subtle emotional states from a person), intra-class dissimilarity (different facial morphology and behaviors of two subjects in one subtle emotion state), and imbalanced sample distribution for each class and subject. This paper aims to solve the last two problems by first employing preprocessing steps: facial registration, cropping and interpolation; and proposes a person-specific AdaBoost classifier with Selective Transfer Machine framework. While preprocessing techniques remove morphological facial differences, the proposed variant of AdaBoost deals with imbalanced characteristics of available subtle expression databases. Performance metrics obtained from experiments on the SMIC and CASME2 spontaneous subtle expression databases confirm that the proposed method improves classification of subtle emotions.
This work was funded by TM under UbeAware project.
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References
Frank, M., Herbasz, M., Sinuk, K., Keller, A., Nolan, C.: I see how you feel: training laypeople and professionals to recognize fleeting emotions. In: The Annual Meeting of the International Communication Association, Sheraton New York, New York City (2009)
Ekman, P.: Lie catching and microexpressions. In: Martin, C. (ed.) The Philosophy of Deception, pp. 118–133. Oxford University, Oxford (2009)
Gottman, J.M., Levenson, R.W.: A two-factor model for predicting when a couple will divorce: exploratory analyses using 14-year longitudinal data*. Fam. Process 41, 83–96 (2002)
Ekman, P.: Microexpression Training Tool (METT). University of California, San Francisco (2002)
Pfister, T., Li, X., Zhao, G., Pietikainen, M.: Recognising spontaneous facial micro-expressions. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1449–1456. IEEE (2011)
Yan, W.J., Wu, Q., Liu, Y.J., Wang, S.J., Fu, X.: Casme database: a dataset of spontaneous micro-expressions collected from neutralized faces. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–7. IEEE (2013)
Yan, W.J., Li, X., Wang, S.J., Zhao, G., Liu, Y.J., Chen, Y.H., Fu, X.: CASME II: an improved spontaneous micro-expression database and the baseline evaluation: an improved spontaneous micro-expression database and the baseline evaluation. PloS one 9, e86041 (2014)
Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: 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 (CVPRW), pp. 94–101. IEEE (2010)
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27, 803–816 (2009)
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)
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29, 915–928 (2007)
Goshtasby, A.: Image registration by local approximation methods. Image Vis. Comput. 6, 255–261 (1988)
Zhou, Z., Zhao, G., Pietikainen, M.: Towards a practical lipreading system. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 137–144. IEEE (2011)
Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29, 40–51 (2007)
He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1208–1213. IEEE (2005)
Ojala, T., Pietikäinen, M., Mäenpää, T.: A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: Singh, S., Murshed, N., Kropatsch, W.G. (eds.) ICAPR 2001. LNCS, vol. 2013, p. 397. Springer, Heidelberg (2001)
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikainen, M.: A spontaneous micro-expression database: Inducement, collection and baseline. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE (2013)
Brody, L.R., Brody, L.R.: On understanding gender differences in the expression of emotion. In: Ablon, S.L., Brown, D., Khantzian, E.J., Mack, J.E. (eds.) Human feelings: Explorations in Affect Development and Meaning. Analytic Press, Hillsdale (1993)
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1521–1528. IEEE (2011)
Aytar, Y., Zisserman, A.: Tabula rasa: model transfer for object category detection. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2252–2259. IEEE (2011)
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the damage of dataset bias. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 158–171. Springer, Heidelberg (2012)
Appel, R., Fuchs, T., Dollár, P., Perona, P.: Quickly boosting decision trees-pruning underachieving features early. In: JMLR Workshop and Conference Proceedings, vol. 28, pp. 594–602 (2013). (JMLR)
Gorski, J., Pfeuffer, F., Klamroth, K.: Biconvex sets and optimization with biconvex functions: a survey and extensions. Math. Methods Oper. Res. 66, 373–407 (2007)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2011)
Chu, W.S., Torre, F.D.L., Cohn, J.F.: Selective transfer machine for personalized facial action unit detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3515–3522. IEEE (2013)
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45, 427–437 (2009)
Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200. ACM (2007)
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Le Ngo, A.C., Phan, R.CW., See, J. (2015). Spontaneous Subtle Expression Recognition: Imbalanced Databases and Solutions. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_3
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