Measuring facial movement, Environmental Psychology and Nonverbal Behavior, vol.37, issue.1, pp.56-75, 1976. ,
DOI : 10.1007/BF01115465
Real-time inference of complex mental states from facial expressions and head gestures, in: Real-time vision for human-computer interaction, pp.181-200, 2005. ,
Detecting depression from facial actions and vocal prosody, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp.1-7, 2009. ,
DOI : 10.1109/ACII.2009.5349358
The painful face ??? Pain expression recognition using active appearance models, Image and Vision Computing, vol.27, issue.12, pp.1788-1796, 2009. ,
DOI : 10.1016/j.imavis.2009.05.007
Continuous Pain Intensity Estimation from Facial Expressions, Advances in Visual Computing, pp.368-377, 2012. ,
DOI : 10.1007/978-3-642-33191-6_36
Affectiva-mit facial expression dataset (am-fed): Naturalistic and spontaneous facial expressions collected " in-thewild, Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on, pp.881-888, 2013. ,
The FaceReader, Proceedings of the 4th Nordic conference on Human-computer interaction changing roles, NordiCHI '06, pp.457-460, 2006. ,
DOI : 10.1145/1182475.1182536
Recognising spontaneous facial micro-expressions, 2011 International Conference on Computer Vision, pp.1449-1456, 2011. ,
DOI : 10.1109/ICCV.2011.6126401
Exploring temporal patterns in classifying frustrated and delighted smiles, Affective Computing, IEEE Transactions on, vol.3, issue.3, pp.323-334, 2012. ,
Facial action unit recognition by exploiting their dynamic and semantic relationships, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.29, issue.10, pp.1683-1699, 2007. ,
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
Recognizing partial facial action units based on 3D dynamic range data for facial expression recognition, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp.1-8, 2008. ,
DOI : 10.1109/AFGR.2008.4813336
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
Data-free prior model for facial action unit recognition, Affective Computing, IEEE Transactions on, vol.4, issue.2, pp.127-141, 2013. ,
Spontaneous facial expression recognition: A robust metric learning approach, Pattern Recognition ,
Continuous AU intensity estimation using localized, sparse facial feature space, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp.1-7, 2013. ,
DOI : 10.1109/FG.2013.6553808
Selective Transfer Machine for Personalized Facial Action Unit Detection, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013. ,
DOI : 10.1109/CVPR.2013.451
Boosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-6, 2007. ,
DOI : 10.1109/CVPR.2007.383059
Recognizing facial action units using independent component analysis and support vector machine, Pattern Recognition, vol.39, issue.9, pp.1795-1798, 2006. ,
DOI : 10.1016/j.patcog.2006.03.017
Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, p.511, 2001. ,
DOI : 10.1109/CVPR.2001.990517
A desicion-theoretic generalization of on-line learning and an application to boosting, Computational learning theory, pp.23-37, 1995. ,
DOI : 10.1007/3-540-59119-2_166
Combining AAM coefficients with LGBP histograms in the multi-kernel SVM framework to detect facial action units, Face and Gesture 2011, pp.860-865, 2011. ,
DOI : 10.1109/FG.2011.5771363
URL : https://hal.archives-ouvertes.fr/hal-00657734
Kernel Conditional Ordinal Random Fields for Temporal Segmentation of Facial Action Units, Computer Vision?ECCV 2012. Workshops and Demonstrations, pp.260-269, 2012. ,
DOI : 10.1007/978-3-642-33868-7_26
Dynamics of facial expression extracted automatically from video, Image and Vision Computing, vol.24, issue.6, pp.615-625, 2006. ,
DOI : 10.1016/j.imavis.2005.09.011
Comprehensive database for facial expression analysis, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp.46-53, 2000. ,
DOI : 10.1109/AFGR.2000.840611
The cmu pose, illumination, and expression database, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.25, issue.12, pp.1615-1618, 2003. ,
The first facial expression recognition and analysis challenge, Face and Gesture 2011, pp.921-926, 2011. ,
DOI : 10.1109/FG.2011.5771374
Fully Automatic Recognition of the Temporal Phases of Facial Actions, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.1, pp.28-43, 2012. ,
DOI : 10.1109/TSMCB.2011.2163710
Dynamics of facial expression extracted automatically from video, Image and Vision Computing, vol.24, issue.6, pp.615-625, 2006. ,
DOI : 10.1016/j.imavis.2005.09.011
Non-rigid registration using free-form deformations for recognition of facial actions and their temporal dynamics, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp.1-8, 2008. ,
DOI : 10.1109/AFGR.2008.4813361
Analyzing Facial Expression by Fusing Manifolds, pp.621-630, 2007. ,
DOI : 10.1007/978-3-540-76390-1_61
Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders, Journal of Neuroscience Methods, vol.200, issue.2, pp.237-256, 2011. ,
DOI : 10.1016/j.jneumeth.2011.06.023
Bosphorus Database for 3D Face Analysis, Biometrics and Identity Management, pp.47-56, 2008. ,
DOI : 10.1007/978-3-540-89991-4_6
The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression , in: Computer Vision and Pattern Recognition Workshops, 2010 IEEE Computer Society Conference on, pp.94-101, 2010. ,
Painful data: The unbc-mcmaster shoulder pain expression archive database, in: Automatic Face & Gesture Recognition and Workshops, 2011 IEEE International Conference on, pp.57-64, 2011. ,
A framework for automated measurement of the intensity of nonposed facial action units, in: Computer Vision and Pattern Recognition Workshops, IEEE Computer Society Conference on, pp.74-80, 2009. ,
Facial action units intensity estimation by the fusion of features with multikernel support vector machine, Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on, pp.1-6, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01126775
Continuous Pain Intensity Estimation from Facial Expressions, Advances in Visual Computing, pp.368-377, 2012. ,
DOI : 10.1007/978-3-642-33191-6_36
Latent trees for estimating intensity of Facial Action Units, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.296-304, 2015. ,
DOI : 10.1109/CVPR.2015.7298626
Deep learning based FACS Action Unit occurrence and intensity estimation, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp.1-5, 2015. ,
DOI : 10.1109/FG.2015.7284873
Context-sensitive dynamic ordinal regression for intensity estimation of facial action units, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.37, issue.5, pp.944-958, 2015. ,
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
Estimating smile intensity: A better way, Pattern Recognition Letters, vol.66 ,
DOI : 10.1016/j.patrec.2014.10.004
Continuous Conditional Neural Fields for Structured Regression, Computer Vision?ECCV 2014, pp.593-608, 2014. ,
DOI : 10.1007/978-3-319-10593-2_39
An argument for basic emotions, Cognition & Emotion, vol.6, issue.3, pp.169-200, 1992. ,
DOI : 10.1080/02699939208411068
Deblurring using regularized locally adaptive kernel regression, Image Processing, IEEE Transactions on, vol.17, issue.4, pp.550-563, 2008. ,
Coronary Lumen Segmentation Using Graph Cuts and Robust Kernel Regression, Information Processing in Medical Imaging, pp.528-539, 2009. ,
DOI : 10.1016/j.jacc.2005.03.067
Robust continuous prediction of human emotions using multiscale dynamic cues, Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI '12, pp.2012-501 ,
DOI : 10.1145/2388676.2388783
Metric learning for kernel regression, International Conference on Artificial Intelligence and Statistics, pp.612-619, 2007. ,
On Estimating Regression, Theory of Probability & Its Applications, vol.9, issue.1, pp.141-142, 1964. ,
DOI : 10.1137/1109020
Feature selection for high-dimensional data: A fast correlation-based filter solution, pp.856-863, 2003. ,
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological ), pp.267-288, 1996. ,
An introduction to variable and feature selection, The Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003. ,
Supervised Descent Method and Its Applications to Face Alignment, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.532-539, 2013. ,
DOI : 10.1109/CVPR.2013.75
Locating facial landmarks with binary map cross-correlations, 2013 IEEE International Conference on Image Processing, pp.2978-2982, 2013. ,
DOI : 10.1109/ICIP.2013.6738613
Effectiveness of Eigenspaces for Facial Expressions Recognition, International Journal of Computer Theory and Engineering, vol.1, issue.5, pp.1793-8201, 2009. ,
DOI : 10.7763/IJCTE.2009.V1.103
Intraclass correlations: Uses in assessing rater reliability., Psychological Bulletin, vol.86, issue.2, 1979. ,
DOI : 10.1037/0033-2909.86.2.420
Facing Imbalanced Data--Recommendations for the Use of Performance Metrics, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp.245-251, 2013. ,
DOI : 10.1109/ACII.2013.47
Markov Random Field Structures for Facial Action Unit Intensity Estimation, 2013 IEEE International Conference on Computer Vision Workshops, pp.738-745, 2013. ,
DOI : 10.1109/ICCVW.2013.101
Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research, vol.7, pp.1-30, 2006. ,