Abstract
One of important cues of deception detection is micro-expression. It has three characteristics: short duration, low intensity and usually local movements. These characteristics imply that micro-expression is sparse. In this paper, we use the sparse part of Robust PCA (RPCA) to extract the subtle motion information of micro-expression. The local texture features of the information are extracted by Local Spatiotemporal Directional Features (LSTD). In order to extract more effective local features, 16 Regions of Interest (ROIs) are assigned based on the Facial Action Coding System (FACS). The experimental results on two micro-expression databases show the proposed method gain better performance. Moreover, the proposed method may further be used to extract other subtle motion information (such as lip-reading, the human pulse, and micro-gesture etc.) from video.
Chapter PDF
Similar content being viewed by others
Keywords
References
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Computer vision and image understanding 61(1), 38–59 (1995)
Dao, M., Suo, Y., Chin, S., Tran, T.: Video frame interpolation via weighted robust principal component analysis. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1404–1408. IEEE (2013)
Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1(3), 211–218 (1936)
Ekman, P.: Microexpression training tool (METT). University of California, San Francisco (2002)
Ekman, P.: Lie catching and microexpressions. The philosophy of deception pp. 118–133 (2009)
Ekman, P., Friesen, W.: Nonverbal leakage and clues to deception. Tech. rep, DTIC Document (1969)
Ekman, P., Friesen, W.V.: Facial action coding system: A technique for the measurement of facial movement, vol. 12. Consulting Psychologists Press, CA (1978)
Georgieva, P., De la Torre, F.: Robust principal component analysis for brain imaging. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds.) ICANN 2013. LNCS, vol. 8131, pp. 288–295. Springer, Heidelberg (2013)
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikäinen, M.: A spontaneous micro-expression database: inducement, collection and baseline. In: IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (2013)
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: Neural Information Processing Systems (NIPS) (2011)
Matsumoto, D., Hwang, H.: Evidence for training the ability to read microexpressions of emotion. Motivation and Emotion 35(2), 181–191 (2011)
Michael, N., Dilsizian, M., Metaxas, D., Burgoon, J.K.: Motion profiles for deception detection using visual cues. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 462–475. Springer, Heidelberg (2010)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ojansivu, V., Heikkilä, J.: Blur Insensitive Texture Classification Using Local Phase Quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008)
Pfister, T., Li, X., Zhao, G., Pietikainen, M.: Recognising spontaneous facial micro-expressions. In: 12th IEEE International Conference on Computer Vision. pp. 1449–1456. IEEE (2011)
Polikovsky, S., Kameda, Y., Ohta, Y.: Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor. In: 3rd International Conference on Crime Detection and Prevention. pp. 1–6. IET (2009)
Shi, L.C., Duan, R.N., Lu, B.L.: A robust principal component analysis algorithm for eeg-based vigilance estimation. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6623–6626. IEEE (2013)
Wang, L., Cheng, H.: Robust principal component analysis for sparse face recognition. In: 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), pp. 171–176. IEEE (2013)
Wang, S.J., Chen, H.L., Yan, W.J., Chen, Y.H., Fu, X.: Face recognition and micro-expression based on discriminant tensor subspace analysis plus extreme learning machine. Neural Processing Letters 39(1), 25–43 (2014)
Wright, J., Ganesh, A., Rao, S., Peng, Y., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. In: Advances in neural information processing systems, pp. 2080–2088 (2009)
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. PLoS ONE 9(1), e86041 (2014)
Yan, W.J., Wu, Q., Liang, J., Chen, Y.H., Fu, X.: How fast are the leaked facial expressions: The duration of micro-expressions. Journal of Nonverbal Behavior, pp. 1–14 (2013)
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 915–928 (2007)
Zhao, G., Pietikäinen, M.: Visual speaker identification with spatiotemporal directional features. In: Kamel, M., Campilho, A. (eds.) ICIAR 2013. LNCS, vol. 7950, pp. 1–10. Springer, Heidelberg (2013)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, SJ., Yan, WJ., Zhao, G., Fu, X., Zhou, CG. (2015). Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-16178-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16177-8
Online ISBN: 978-3-319-16178-5
eBook Packages: Computer ScienceComputer Science (R0)