Tian Chang
ABSTRACT
Micro-expression recognition attracts more and more attentions from computer vision due to its wide range of potential applications. This paper proposes an approach for micro-expression recognition, Local Optical Flow Binary Patterns on Three Orthogonal Planes (LOFBP-TOP), by combining optical flow and LBP-TOP. We first compute optical flow on cropped micro-expression videos, then we encode the magnitude and orientation component of optical flow with LBP-TOP to get LOFBP-TOP features. Furthermore, ReliefF is used to do feature selection on LOFBP-TOP features. Finally, SVM is used in classification. Experimental results on two datasets, CASME2 and SMIC, show that our method has better recognition performance than some popular methods in micro-expression recognition, such as LBP-TOP and HOOF.
- E. A. Haggard and K. S. Isaacs. 1966. "Micromomentary facial expressions as indicators of ego mechanisms in psychotherapy," in Methods of Research in Psychotherapy, Springer, pp. 154--165.Google Scholar
- P. Ekman. 2002. Micro Expression training tool (METT), San Francisco: University of California.Google Scholar
- Yan W J, Li X B, Wang S J, Zhao G Y, Liu Y J, Chen Y H and Fu X L. 2014. CASME II: An improved spontaneous micro-expression database and the baseline evaluation. PLoS One, 9(1): e86041Google ScholarCross Ref
- X. Li, T. Pfister, X. Huang, G. Zhao, and M. Pietik¨ainen. 2013. "A spontaneous micro-expression database: Inducement, collection and baseline," In Proc. AFGR, 2013:1--6.Google Scholar
- Polikovsky, Senya, Y. Kameda, and Y. Ohta. 2010. "Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor," International Conference on Crime Detection and Prevention IET, 1--6.Google Scholar
- Pfister T, Li X, Zhao G, et al. 2011. Recognizing spontaneous facial micro-expressions{C}// Proceedings of the 2011 International Conference on Computer Vision. IEEE Computer Society, 2011:1449 -- 1456. Google ScholarDigital Library
- Y. Wang, J. See, R. C.-W. Phan, and Y.-H. Oh. 2015. "Efficient spatiotemporal local binary patterns for spontaneous facial micro-expression recognition", PloSone, vol.10, no.5, p. e0124674.Google Scholar
- Huang X H, Zhao G Y, Hong X P, Zheng W M, Pietik¨ainen M. 2016. Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns. Neurocomputing, pp. 564--578. Google ScholarDigital Library
- Huang X H, Zhao G Y, Hong X P, Pietik¨inen M, Zheng W M. 2013. Texture description with completed local quantized patterns, Image Analysis, Berlin Heidelberg: Springer, pp.1 --10.Google Scholar
- R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal. 2009. "Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions," in 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 1932--1939.Google Scholar
- Liu Y J, Zhang J K, Yan W J, Wang S J, Zhao G Y and Fu X L. 2016. A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition, IEEE Transactions on Affective Computing, 7(4), pp. 299--310. Google ScholarDigital Library
- Xu F, Zhang J, Wang J Z. 2017. Micro-expression Identification and Categorization Using a Facial Dynamics Map. IEEE Transactions on Affective Computing, pp.1--1.Google Scholar
- R. Garg, A. Roussos, L. Agapito. 2013. "A Variational Approach to Video Registration with Subspace Constraints", International Journal of Computer Vision (IJCV), vol. 104, issue 3, pp. 286--314.Google ScholarCross Ref
- Kononeill I. 1994. Estimating attributes: Analysis and extensions of RELIEF. the Proceedings of the 1994 European Conference on Machine Learning, pp. 171--182. Google ScholarDigital Library
Recommendations
Local binary patterns for multi-view facial expression recognition
Research into facial expression recognition has predominantly been applied to face images at frontal view only. Some attempts have been made to produce pose invariant facial expression classifiers. However, most of these attempts have only considered ...
A Survey of Micro-expression Recognition Methods Based on LBP, Optical Flow and Deep Learning
AbstractMicro-expressions typically reflect suppressed feelings and they can provide an accurate indication of the real feelings and motivations of a person. Accordingly, they have been used for clinical diagnosis, business negotiations, interrogations, ...
Micro-expression recognition based on 3D flow convolutional neural network
AbstractMicro-expression recognition (MER) is a growing field of research which is currently in its early stage of development. Unlike conventional macro-expressions, micro-expressions occur at a very short duration and are elicited in a spontaneous ...
Comments