Abstract
Micro-expressions are the subtle and short-lived facial deformations that convey the inner feelings of a person. Automatic recognition of micro-expressions has potential applications in many areas. However, extraction of the appropriate feature, for encoding the subtle movements during the micro-expressions, is a very challenging work. The use of spatial and spatio-temporal features are studied extensively for this problem. However, the face appearance does not change appreciably during a micro-expression. Moreover, the muscle movements are also very small, almost indistinguishable. Rather, these changes possess a temporal pattern. We use the fuzzy histogram of optical flow orientation (FHOFO) features to encode the temporal patterns associated with facial micro-movements. The FHOFO constructs fuzzified angular histograms from the facial movement vectors. The feature descriptors of a micro-expression clip usually possess high dimension and suffer from the curse of dimensionality. To this end, we explore different feature selection methods to reduce the dimension of the descriptor. Experimentally we found that FHOFO achieves significant accuracy on the publicly available databases and its performance is consistently well across the databases.
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References
R.W. Picard, R. Picard, Affective Computing, vol. 252 (MIT Press, Cambridge, 1997)
P. Ekman, Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life (Times Books, New York, 2003)
P. Ekman, Lie catching and microexpressions, The Philosophy of Deception (2009), pp. 118–133
W.-J. Yan, Q. Wu, J. Liang, Y.-H. Chen, X. Fu, How fast are the leaked facial expressions: the duration of micro-expressions. J. Nonverbal Behavior 37(4), 217–230 (2013)
M. Frank, M. Herbasz, K. Sinuk, A. Keller, C. Nolan, I see how you feel: training laypeople and professionals to recognize fleeting emotions, in The Annual Meeting of the International Communication Association, New York City (2009)
E. Sariyanidi, H. Gunes, A. Cavallaro, Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1113–1133 (2015)
Y. Tian, T. Kanade, J.F. Cohn, Facial expression recognition, Handbook of Face Recognition (Springer, Berlin, 2011), pp. 487–519
M. Valstar, M. Pantic, Combined support vector machines and hidden Markov models for modeling facial action temporal dynamics, Human–Computer Interaction (2007), pp. 118–127
S.-T. Liong, J. See, K. Wong, R.C.-W. Phan, Automatic micro-expression recognition from long video using a single spotted apex, in Asian Conference on Computer Vision (Springer, 2016), pp. 345–360
M. Shreve, J. Brizzi, S. Fefilatyev, T. Luguev, D. Goldgof, S. Sarkar, Automatic expression spotting in videos. Image Vis. Comput. 32(8), 476–486 (2014)
D. Patel, G. Zhao, M. Pietikäinen, Spatiotemporal integration of optical flow vectors for micro-expression detection, in International Conference on Advanced Concepts for Intelligent Vision Systems (Springer, 2015), pp. 369–380
S.-J. Wang, S. Wu, X. Qian, J. Li, X. Fu, A main directional maximal difference analysis for spotting facial movements from long-term videos. Neurocomputing 230, 382–389 (2017)
Z. Xia, X. Feng, J. Peng, X. Peng, G. Zhao, Spontaneous micro-expression spotting via geometric deformation modeling. Comput. Vis. Image Underst. 147, 87–94 (2015)
S. Polikovsky, Y. Kameda, Facial micro-expression detection in hi-speed video based on facial action coding system (FACS). IEICE Trans. Inf. Syst. 96(1), 81–92 (2013)
X. Li, H. Xiaopeng, A. Moilanen, X. Huang, T. Pfister, G. Zhao, M. Pietikainen, Towards reading hidden emotions: a comparative study of spontaneous micro-expression spotting and recognition methods. IEEE Trans. Affect. Comput. 8, 29–42 (2017)
A.K. Davison, M.H. Yap, C. Lansley, Micro-facial movement detection using individualised baselines and histogram-based descriptors, in IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2015), pp. 1864–1869
A.C. Le Ngo, Y.-H. Oh, R.C.-W. Phan, J. See, Eulerian emotion magnification for subtle expression recognition, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2016), pp. 1243–1247
K. Sumi, T. Ueda, Micro-expression recognition for detecting human emotional changes, in International Conference on Human-Computer Interaction (Springer, 2016), pp. 60–70
H. Zheng, Micro-expression recognition based on 2D Gabor filter and sparse representation. J. Phys.: Conf. Ser. 787(1), 012013 (2017) (IOP Publishing)
Q. Wu, X. Shen, X. Fu, The machine knows what you are hiding: an automatic micro-expression recognition system, in Affective Computing and Intelligent Interaction (2011), pp. 152–162
M. Owayjan, A. Kashour, N. Al Haddad, M. Fadel, G. Al Souki, The design and development of a lie detection system using facial micro-expressions, in 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA) (IEEE, 2012), pp. 33–38
S. Yao, N. He, H. Zhang, O. Yoshie, Micro-expression recognition by feature points tracking, in 2014 10th International Conference on Communications (COMM) (IEEE, 2014), pp. 1–4
M. Shreve, S. Godavarthy, D. Goldgof, S. Sarkar, Macro-and micro-expression spotting in long videos using spatio-temporal strain, in 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG 2011) (IEEE, 2011), pp. 51–56
S.-T. Liong, J. See, R.C.-W. Phan, A.C. Le Ngo, Y.-H. Oh, K. Wong, Subtle expression recognition using optical strain weighted features, in Asian Conference on Computer Vision (Springer, 2014), pp. 644–657
Y.-J. Liu, J.-K. Zhang, W.-J. Yan, S.-J. Wang, G. Zhao, X. Fu, A main directional mean optical flow feature for spontaneous micro-expression recognition. IEEE Trans. Affect. Comput. 7, 299–310 (2016). https://doi.org/10.1109/TAFFC.2015.2485205
F. Xu, J. Zhang, J.Z. Wang, Microexpression identification and categorization using a facial dynamics map. IEEE Trans. Affect. Comput. 8(2), 254–267 (2017)
X. Huang, G. Zhao, X. Hong, W. Zheng, M. Pietikäinen, Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns. Neurocomputing 175, 564–578 (2016)
S.-J. Wang, W.-J. Yan, G. Zhao, X. Fu, C.-G. Zhou, Micro-expression recognition using robust principal component analysis and local spatiotemporal directional features, in Computer Vision-ECCV Workshops (2014), pp. 325–338
G. Zhao, M. Pietikainen, Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
J. He, J.-F. Hu, X. Lu, W.-S. Zheng, Multi-task mid-level feature learning for micro-expression recognition. Pattern Recognit. 66, 44–52 (2017)
T. Pfister, X. Li, G. Zhao, M. Pietikäinen, Recognising spontaneous facial micro-expressions, in IEEE International Conference on Computer Vision (ICCV) (2011), pp. 1449–1456
W.-J. Yan, X. Li, S.-J. Wang, G. Zhao, Y.-J. Liu, Y.-H. Chen, X. Fu, CASME II: an improved spontaneous micro-expression database and the baseline evaluation. PloS one 9(1), e86041 (2014)
B. Jiang, M. Valstar, B. Martinez, M. Pantic, A dynamic appearance descriptor approach to facial actions temporal modeling. IEEE Trans. Cybern. 44(2), 161–174 (2014)
X. Fan, T. Tjahjadi, A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences. Pattern Recognit. 48(11), 3407–3416 (2015)
X. Huang, G. Zhao, W. Zheng, M. Pietikäinen, Spatiotemporal local monogenic binary patterns for facial expression recognition. IEEE Signal Process. Lett. 19(5), 243–246 (2012)
S. Polikovsky, Y. Kameda, Y. Ohta, Facial micro-expression detection in hi-speed video based on facial action coding system (FACS). IEICE Trans. Inf. Syst. 96(1), 81–92 (2013)
Y. Guo, C. Xue, Y. Wang, M. Yu, Micro-expression recognition based on CBP-TOP feature with ELM. Optik-Int. J. Light Electron Opt. 126(23), 4446–4451 (2015)
X. Ben, X. Jia, R. Yan, X. Zhang, W. Meng, Learning effective binary descriptors for micro-expression recognition transferred by macro-information. Pattern Recognit. Lett. (2017)
S.-J. Wang, H.-L. Chen, W.-J. Yan, Y.-H. Chen, X. Fu, Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine. Neural Process. Lett. 39(1), 25–43 (2014)
S.-J. Wang, W.-J. Yan, X. Li, G. Zhao, C.-G. Zhou, X. Fu, M. Yang, J. Tao, Micro-expression recognition using color spaces. IEEE Trans. Image Process. 24(12), 6034–6047 (2015)
D. Patel, X. Hong, G. Zhao, Selective deep features for micro-expression recognition, in 2016 23rd International Conference on Pattern Recognition (ICPR) (IEEE, 2016), pp. 2258–2263
D.H. Kim, W. Baddar, J. Jang, Y.M. Ro, Multi-objective based spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans. Affect. Comput. (2017)
R. Chaudhry, A. Ravichandran, G. Hager, R. Vidal, Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions, in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1932–1939
X. Li, T. Pfister, X. Huang, G. Zhao, M. Pietikainen, A spontaneous micro-expression database: inducement, collection and baseline, in IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) (2013), pp. 1–6
W.-J. Yan, Q. Wu, Y.-J. Liu, S.-J. Wang, X. Fu, CASME database: a dataset of spontaneous micro-expressions collected from neutralized faces, in IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) (2013), pp. 1–7
J. Tang, S. Alelyani, H. Liu, Feature selection for classification: a review, Data Classification: Algorithms and Applications (2014), p. 37
Z. Zhao, L. Wang, H. Liu, Efficient spectral feature selection with minimum redundancy, in 24th AAAI Conference on Artificial Intelligence (2010)
L. Yu, H. Liu, Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)
L. Yin, Y. Ge, K. Xiao, X. Wang, X. Quan, Feature selection for high-dimensional imbalanced data. Neurocomputing 105, 3–11 (2013)
B.D. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in International Joint Conference on Artificial Intelligence, vol. 81 (1981), pp. 674–679
J. Han, K.-K. Ma, Fuzzy color histogram and its use in color image retrieval. IEEE Trans. Image Process. 11(8), 944–952 (2002)
P.S. Bradley, O.L. Mangasarian, Feature selection via concave minimization and support vector machines, in International Conference on Machine Learning (ICML), vol. 98 (1998), pp. 82–90
X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in NIPS, vol. 186 (2005), p. 189
S.L. Happy, R. Mohanty, A. Routray, An effective feature selection method based on pair-wise feature proximity for high dimensional low sample size data, in European Signal Processing Conference (EUSIPCO) (2017)
S.L. Happy, A. Routray, Automatic facial expression recognition using features of salient facial patches. IEEE Trans. Affect. Comput. 6(1), 1–12 (2015)
M. Shreve, S. Godavarthy, D. Goldgof, S. Sarkar, Macro-and micro-expression spotting in long videos using spatio-temporal strain, in IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (2011), pp. 51–56
S.L. Happy, A. Routray, Fuzzy histogram of optical flow orientations for micro-expression recognition. IEEE Trans. Affect. Comput. (2017)
A. Asthana, S. Zafeiriou, G. Tzimiropoulos, S. Cheng, M. Pantic, From pixels to response maps: discriminative image filtering for face alignment in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1312–1320 (2015)
M. Sokolova, G. Lapalme, A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)
A.C. Le Ngo, R.C.-W. Phan, J. See, Spontaneous subtle expression recognition: imbalanced databases and solutions, in Asian Conference on Computer Vision (Springer, 2014), pp. 33–48
X. Huang, S.-J. Wang, G. Zhao, M. Piteikainen, Facial micro-expression recognition using spatiotemporal local binary pattern with integral projection, in IEEE International Conference on Computer Vision (ICCV) Workshops (2015), pp. 1–9
S.-T. Liong, J. See, R.C.-W. Phan, Y.-H. Oh, A.C. Le Ngo, K. Wong, S.-W. Tan, Spontaneous subtle expression detection and recognition based on facial strain. Signal Process.: Image Commun. 47, 170–182 (2016)
S.-J. Wang, W.-J. Yan, T. Sun, G. Zhao, X. Fu, Sparse tensor canonical correlation analysis for micro-expression recognition. Neurocomputing 214, 218–232 (2016)
W.-J. Yan, S.-J. Wang, Y.-J. Liu, Q. Wu, X. Fu, For micro-expression recognition: database and suggestions. Neurocomputing 136, 82–87 (2014)
Y.-H. Oh, A.C. Le Ngo, J. See, S.-T. Liong, R.C.-W. Phan, H.-C. Ling, Monogenic Riesz wavelet representation for micro-expression recognition, in IEEE International Conference on Digital Signal Processing (DSP) (IEEE, 2015), pp. 1237–1241
Y. Wang, J. See, R.C.-W. Phan, Y.-H. Oh, Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition. PloS one 10(5), e0124674 (2015)
Acknowledgments
The authors would like to thank Ministry of Human Resource Development, Govt. of India for funding this research.
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Happy, S.L., Routray, A. (2018). Recognizing Subtle Micro-facial Expressions Using Fuzzy Histogram of Optical Flow Orientations and Feature Selection Methods. In: Pedrycz, W., Chen, SM. (eds) Computational Intelligence for Pattern Recognition. Studies in Computational Intelligence, vol 777. Springer, Cham. https://doi.org/10.1007/978-3-319-89629-8_13
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