Abstract
The challenges of recognition of spontaneous expressions from spatio-temporal data include the characterization of subtle changes of facial textures, which in many cases occur for a very brief duration. In this context, the paper presents an intelligent approach for spontaneous expression recognition algorithm, wherein adaptive magnification of motion of spatio-temporal data is applied prior to the extraction of features of expression. The proposed magnification enhances the low-intensity facial activities without introducing notable artifacts for the high-intensity activities. The local binary patterns extracted from three-orthogonal planes of the Eulerian magnified spatio-temporal data are used as features of spontaneous expressions. The extracted features are classified using the well-known support vector machine classifier. Experiments are conducted on commonly-referred spatio-temporal databases such as the SMIC and MMI that have spontaneous expressions representing the micro- and meso-level facial activities, respectively. Experimental results reveal that the proposed approach of motion magnification prior to feature extraction significantly improves the detection and classification accuracy at the expense of acceptable robustness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wundt, W.M.: Grundzüge de physiologischen Psychologie. Engelman, Leipzig (1905)
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124–129 (1971)
Wang, Z., Wang, S., Ji, Q.: Capturing complex spatio-temporal relations among facial muscles for facial expression recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, pp. 3422–3429 (2013)
Bartlett, M.S., Littlewort, G.C., Frank, M.G., Lainscsek, C., Fasel, I.R., Movellan, J.R.: Automatic recognition of facial actions in spontaneous expressions. J. Multimedia 1(6), 22–35 (2006)
Koelstra, S., Patras, I.: Fusion of facial expressions and EEG for implicit affective tagging. Image Vis. Comput. 31(2), 164–174 (2013)
Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1424–1445 (2000)
Ghimire, D., Lee, J., Li, Z.N., Jeong, S., Park, S.H., Choi, H.S.: Recognition of facial expressions based on tracking and selection of discriminative geometric features. Int. J. Multimedia Ubiquitous Eng. 10(3), 35–44 (2015)
Ptucha, R., Tsagkatakis, G., Savakis, A.: Manifold based sparse representation for robust expression recognition without neutral subtraction. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, Barcelona, Spain, pp. 2136–2143 (2011)
Kahou, S.E., Froumenty, P., Pal, C.: Facial expression analysis based on high dimensional binary features. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8926, pp. 135–147. Springer, Heidelberg (2015)
Hu, Y., Zeng, Z., Yin, L., Wei, X., Zhou, X., Huang, T.S.: Multi-view facial expression recognition. In: Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition, Amsterdam, Netherlands, pp. 1–6 (2008)
Ji, Y., Idrissi, K.: Automatic facial expression recognition based on spatiotemporal descriptors. Pattern Recogn. Lett. 33(10), 1373–1380 (2012)
Sariyanidi, E., Gunes, H., Cavallaro, A.: Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1113–1133 (2015)
Wang, S., Ji, Q.: Video affective content analysis: a survey of state-of-the-art methods. IEEE Trans. Affect. Comput. 6(4), 410–430 (2015)
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikäinen, M.: A spontaneous micro-expression database: inducement, collection and baseline. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, Shanghai, China, pp. 1–6 (2013)
Shreve, M., Godavarthy, S., Goldgof, D., Sarkar, S.: Macro- and micro-expression spotting in long videos using spatio-temporal strain. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, Santa Barbara, pp. 51–56 (2011)
Polikovsky, S., Kameda, Y., Ohta, Y.: Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor. In: Proceedings of the IET IEEE International Conference on Crime Detection and Prevention, London, UK, pp. 1–6 (2009)
Gogia, S., Liu, R.: Motion magnification of facial micro-expressions. Technical report 4, Massachusetts Institute of Technology (2014). http://runpeng.mit.edu/project#research
Park, S.Y., Lee, S.H., Ro, Y.M.: Subtle facial expression recognition using adaptive magnification of discriminative facial motion. In: Proceedings of the ACM IEEE International Conference on Multimedia, pp. 911–914 (2015)
Akagi, Y., Kawasaki, H.: A method of micro facial expression recognition based on dense facial motion data. In: Proceedings of the IEEE International Conference on Central Europeon Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic, pp. 39–44 (2014)
Park, S., Kim, D.: Subtle facial expression recognition using motion magnification. Pattern Recogn. Lett. 30(7), 708–716 (2009)
Li, X., Hong, X., Moilanen, A., Huang, X., Pfister, T., Zhao, G., Pietikäinen, M.: Reading hidden emotions: spontaneous micro-expression spotting and recognition. Technical report 1511.00423v1, Cornell University, arXiv e-prints (2015)
Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 1–8 (2012)
Chan, S.H., Vo, D.T., Nguyen, T.Q.: Subpixel motion estimation without interpolation. In: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, Dallas, TX, pp. 722–725 (2010)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
Pantic, M., Valstar, M., Rademaker, R., Maat, L.: Web-based database for facial expression analysis. In: Proceedings of the IEEE International Conference on Multimedia and Expo, Amsterdam, The Netherlands, pp. 1–5 (2005)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Talukder, B.M.S.B., Chowdhury, B., Howlader, T., Rahman, S.M.M. (2016). Intelligent Recognition of Spontaneous Expression Using Motion Magnification of Spatio-temporal Data. In: Chau, M., Wang, G., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2016. Lecture Notes in Computer Science(), vol 9650. Springer, Cham. https://doi.org/10.1007/978-3-319-31863-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-31863-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31862-2
Online ISBN: 978-3-319-31863-9
eBook Packages: Computer ScienceComputer Science (R0)