Abstract
Optical strain characterizes the relative amount of displacement by a moving object within a time interval. Its ability to compute any small muscular movements on faces can be advantageous to subtle expression research. This paper proposes a novel optical strain weighted feature extraction scheme for subtle facial micro-expression recognition. Motion information is derived from optical strain magnitudes, which is then pooled spatio-temporally to obtain block-wise weights for the spatial image plane. By simple product with the weights, the resulting feature histograms are intuitively scaled to accommodate the importance of block regions. Experiments conducted on two recent spontaneous micro-expression databases–CASMEII and SMIC, demonstrated promising improvement over the baseline results.
Work done in project UbeAware funded by TM.
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References
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971)
Ekman, P.: Lie catching and microexpressions. In: Martin, C. (ed.) The Philosophy of Deception, pp. 118–133. Oxford University Press, New York (2009)
Porter, S., ten Brinke, L.: Reading between the lies identifying concealed and falsified emotions in universal facial expressions. Psych. Sci. 19, 508–514 (2008)
Frank, M.G., Herbasz, M., Sinuk, K., Keller, A., Kurylo, A., Nolan, C.: I see how you feel: training laypeople and professionals to recognize fleeting emotions. In: Annual Meeting of the International Communication Association, Sheraton New York, New York City, NY (2009)
O’Sullivan, M., Frank, M.G., Hurley, C.M., Tiwana, J.: Police lie detection accuracy: the effect of lie scenario. Law Hum. Behav. 33(6), 530–538 (2009)
Frank, M.G., Maccario, C.J., Govindaraju, V.: Protecting Airline Passengers in the Age of Terrorism. ABC-CLIO, Santa Barbara (2009)
D’hooge, J., Heimdal, A., Jamal, F., Kukulski, T., Bijnens, B., Rademakers, F., Hatle, L., Suetens, P., Sutherland, G.R.: Regional strain and strain rate measurements by cardiac ultrasound: principles, implementation and limitations. Eur. J. Echocardiogr. 1(3), 154–170 (2000)
Shreve, M., Manohar, V., Goldgof, D., Sarkar, S.: Face recognition under camouflage and adverse illumination. In: 4th IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–6 (2010)
Manohar, V., Goldgof, D., Sarkar, S.: Facial strain pattern as a soft forensic evidence. In: Applications of Computer Vision (WACV) (2007)
Shreve, M., Godavarthy, S., Manohar, V., Goldgof, D., Sarkar, S.: Towards macro-and micro-expression spotting in video using strain patterns. In: Applications of Computer Vision (WACV), pp. 1–6 (2009)
Shreve, M., Godavarthy, S., Goldgof, D., Sarkar, S.: Macro-and micro-expression spotting in long videos using spatio- temporal strain. In: Automatic Face, Gesture Recognition and Workshops, pp. 51–56 (2011)
Vinciarelli, A., Dielmann, A., Favre, S., Salamin, H.: Canal9: a database of political debates for analysis of social interactions. In: Affective Computing and Intelligent Interaction and Workshops, pp. 1–4 (2009)
Ekman, P.: Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage. W. W. Norton and Company, New York (2009)
Shreve, M., Brizzi, J., Fefilatyev, S., Luguev, T., Goldgof, D., Sarkar, S.: Automatic expression spotting in videos. Image Vis. Comput. 32(8), 476–486 (2014)
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 20(6), 915–928 (2007)
Yan, W.J., Wang, S.J., Zhao, G., Li, X., Liu, Y.J., Chen, Y.H., Fu, X.: Casme ii: an improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE 9, e86041 (2014)
Pfister, T., Li, X., Zhao, G., Pietikainen, M.: Recognising spontaneous facial micro-expressions. In: Computer Vision (ICCV), pp. 1449–1456 (2011)
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikainen, M.: A spontaneous micro-expression database: inducement, collection and baseline. In: Automatic Face and Gesture Recognition, pp. 1–6 (2013)
Boureau, Y.L., Ponce, J., LeCun, Y.: A theoretical analysis of feature pooling in visual recognition. In: Machine Learning (ICML 2010), pp. 11–118 (2010)
Hamel, P., Lemieux, S., Bengio, Y., Eck, D.: Temporal pooling and multiscale learning for automatic annotation and ranking of music audio. In: International Society for Music Information Retrieval Conference, pp. 729–734 (2011)
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River (2002)
Ekman, P., Friesen, W.V.: Facial Action Coding System. Consulting Psychologists Press, Palo Alto (1978)
Lien, J.J.J., Kanade, T., Cohn, J.F., Li, C.C.: Detection, tracking, and classification of action units in facial expression. Rob. Auton. Syst. 31(3), 131–146 (2000)
Liu, Z., Shan, Y., Zhang, Z.: Expressive expression mapping with ratio images. In: Computer Graphics and Interactive Techniques, pp. 271–276 (2001)
Anitha, C., Venkatesha, M.K., Adiga, B.S.: A survey on facial expression databases. Int. J. Eng. Sci. Technol. 2(10), 5158–5174 (2010)
Yan, W.J., Wang, S.J., Liu, Y.J., Wu, Q., Fu, X.: For micro-expression recognition: database and suggestions. Neurocomputing 136, 82–87 (2014)
Polikovsky, S., Kameda, Y.,O.Y.: Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor. In: Crime Detection and Prevention
Warren, G., Schertler, E., Bull, P.: Detecting deception from emotional and unemotional cues. J. Nonverbal Behav. 33(1), 59–59 (2009)
Barron, J.L., Thacker, N.A.: Tutorial: Computing 2D and 3D optical flow (2005) Imaging Science and Biomedical Engineering Division, Medical School, University of Manchester
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision, vol. 5. McGraw-Hill Education, New York (1995)
Godavarthy, S.: Microexpression spotting in video using optical strain. Masters thesis, University of South Florida (2010)
Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: Computer Vision and Pattern Recognition, pp. 2432–2439 (2010)
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Liong, ST., See, J., Phan, R.CW., Le Ngo, A.C., Oh, YH., Wong, K. (2015). Subtle Expression Recognition Using Optical Strain Weighted Features. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_47
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