O. Rudovic, V. Pavlovic, and M. Pantic, Context-Sensitive Dynamic Ordinal Regression for Intensity Estimation of Facial Action Units, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.5, pp.1-1, 2014.
DOI : 10.1109/TPAMI.2014.2356192

A. Mehrabian and M. Wiener, Decoding of inconsistent communications., Journal of Personality and Social Psychology, vol.6, issue.1, p.109, 1967.
DOI : 10.1037/h0024532

M. Pantic, Machine analysis of facial behaviour: naturalistic and dynamic behaviour, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.27, issue.4, pp.3505-3513, 1535.
DOI : 10.1017/S0140525X02000080

Z. Hammal and J. F. Cohn, Automatic detection of pain intensity, Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI '12
DOI : 10.1145/2388676.2388688

J. Whitehill, G. Littlewort, I. Fasel, M. Bartlett, and J. Movellan, Toward Practical Smile Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.11, pp.312106-2111, 2009.
DOI : 10.1109/TPAMI.2009.42

T. R. Almaev and M. Valstar, Local Gabor Binary Patterns from Three Orthogonal Planes for Automatic Facial Expression Recognition, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2013.
DOI : 10.1109/ACII.2013.65

P. Ekman and W. V. Friesen, Manual for the facial action coding system, 1978.

J. C. Hager, P. Ekman, and W. Friesen, Facial action coding system, 2002.

Z. Zhang, M. Lyons, M. Schuster, and S. Akamatsu, Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998.
DOI : 10.1109/AFGR.1998.670990

G. Zhao and M. Pietikainen, Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.6, pp.915-928, 2007.
DOI : 10.1109/TPAMI.2007.1110

Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, A survey of affect recognition methods, Proceedings of the ninth international conference on Multimodal interfaces , ICMI '07, pp.39-58, 2009.
DOI : 10.1145/1322192.1322216

P. Lucey, J. F. Cohn, K. M. Prkachin, P. E. Solomon, and I. Matthews, Painful data: The UNBC-McMaster shoulder pain expression archive database, Face and Gesture 2011, 2011.
DOI : 10.1109/FG.2011.5771462

L. F. Barrett, Was Darwin Wrong About Emotional Expressions?, Current Directions in Psychological Science, vol.98, issue.6, 2011.
DOI : 10.1038/nn.2138

M. Valstar and M. Pantic, Fully automatic facial action unit detection and temporal analysis Action unit detection using sparse appearance descriptors in space-time video volumes, Proc. IEEE CVPR Proc. IEEE ICFG, 2006.

S. D. Gunnery, J. A. Hall, and M. A. Ruben, The Deliberate Duchenne Smile: Individual Differences in Expressive Control, Journal of Nonverbal Behavior, vol.32, issue.1, pp.29-41, 2013.
DOI : 10.1007/s10919-012-0139-4

M. Valstar, J. Girard, T. Almaev, G. Mckeown, M. Mehu et al., FERA 2015 - second Facial Expression Recognition and Analysis challenge, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015.
DOI : 10.1109/FG.2015.7284874

S. M. Mavadati, M. H. Mahoor, K. Bartlett, P. Trinh, and J. Cohn, DISFA: A Spontaneous Facial Action Intensity Database, IEEE Transactions on Affective Computing, vol.4, issue.2, pp.151-160, 2013.
DOI : 10.1109/T-AFFC.2013.4

S. Kaltwang, O. Rudovic, and M. Pantic, Continuous Pain Intensity Estimation from Facial Expressions, Advances in Visual Computing, pp.368-377, 2012.
DOI : 10.1007/978-3-642-33191-6_36

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.371.1809

T. Senechal, V. Rapp, H. Salam, R. Seguier, K. Bailly et al., Facial Action Recognition Combining Heterogeneous Features via Multikernel Learning, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.4, pp.993-1005, 2012.
DOI : 10.1109/TSMCB.2012.2193567

URL : https://hal.archives-ouvertes.fr/hal-00731864

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

T. F. Cootes, G. J. Edwards, and C. J. Taylor, Active appearance models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.6, pp.681-685, 2001.
DOI : 10.1109/34.927467

S. W. Chew, P. Lucey, S. Lucey, J. Saragih, J. F. Cohn et al., Person-independent facial expression detection using Constrained Local Models, Face and Gesture 2011, 2011.
DOI : 10.1109/FG.2011.5771373

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.688.9772

S. Maji, A. Berg, and J. Malik, Classification using intersection kernel support vector machines is efficient, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587630

F. R. Bach, G. R. Lanckriet, and M. I. Jordan, Multiple kernel learning, conic duality, and the SMO algorithm, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015424

Y. Zhang and Q. Ji, Active and dynamic information fusion for facial expression understanding from image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.5, pp.699-714, 2005.
DOI : 10.1109/TPAMI.2005.93

M. H. Mahoor, S. Cadavid, D. S. Messinger, and J. F. Cohn, A framework for automated measurement of the intensity of non-posed Facial Action Units, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009.
DOI : 10.1109/CVPRW.2009.5204259

A. Savran, B. Sankur, and M. T. Bilge, Regression-based intensity estimation of facial action units, Image and Vision Computing, vol.30, issue.10, pp.774-784, 2012.
DOI : 10.1016/j.imavis.2011.11.008

L. A. Jeni, J. M. Girard, J. F. Cohn, and F. De-la-torre, Continuous AU intensity estimation using localized, sparse facial feature space, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013.
DOI : 10.1109/FG.2013.6553808

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.814

P. Liu, S. Han, Z. Meng, and Y. Tong, Facial Expression Recognition via a Boosted Deep Belief Network, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.233

W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition, Proc. IEEE ICCV, 2005.

X. Xiong and F. De-la-torre, Supervised Descent Method and Its Applications to Face Alignment, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.75

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, p.27, 2011.
DOI : 10.1145/1961189.1961199

G. R. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, Learning the kernel matrix with semidefinite programming, JMLR, vol.5, pp.27-72, 2004.

S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, Large scale multiple kernel learning, JMLR, vol.7, pp.1531-1565, 2006.

X. Zhang, L. Yin, J. F. Cohn, S. Canavan, M. Reale et al., BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database, Image and Vision Computing, vol.32, issue.10, pp.32692-706, 2014.
DOI : 10.1016/j.imavis.2014.06.002

P. E. Shrout and J. L. Fleiss, Intraclass correlations: Uses in assessing rater reliability., Psychological Bulletin, vol.86, issue.2, p.420, 1979.
DOI : 10.1037/0033-2909.86.2.420