J. Nicolle, Using Hard Multi-Task Metric Learning for Kernel Regression, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01365433

J. Nicolle, K. Bailly, and M. Chetouani, Real-time facial action unit intensity prediction with regularized metric learning, Image Vision Computing, vol.52, issue.1, pp.1-14, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01318177

J. Nicolle, K. Bailly, and M. Chetouani, Facial Action Unit intensity prediction via Hard Multi-Task Metric Learning for Kernel Regression, Facial Expression Recognition and Analysis Challenge (FE-RA, 2015.

J. Nicolle, V. Rapp, K. Bailly, L. Prevost, and M. Chetouani, Combining LGBP Histograms with AAM coefficients in the Multi-Kernel SVM framework to detect Facial Action Units, Facial Expression Recognition and Analysis Challenge (FERA'2011)

T. Senechal, K. Bailly, and L. Prevost, Robust continuous prediction of human emotions using multiscale dynamic cues, The Continuous Audio/Visual Emotion Challenge, 2012.

A. Dapogny, A walk through randomness for face analysis in unconstrained environments, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01588960

A. Dapogny, K. Bailly, and S. Dubuisson, Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit Detection, International Journal of Computer Vision (IJCV), vol.126, issue.2-4, pp.255-271, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01509850

A. Dapogny, Bailly Face Alignment with Cascaded Semi-Parametric Deep Greedy Neural Forests, Pattern Recognition Letters (PRL), vol.102, issue.1, pp.75-81, 2018.

A. Dapogny, K. Bailly, and S. Dubuisson, CDynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests, IEEE Transactions on Affective Computing (TAC), vol.126, issue.2-4, pp.255-271, 2018.

A. Dapogny and K. Bailly, Investigating Deep Neural Forests for Facial Expression Recognition, Automatic Face and Gesture Recognition (FG, 2018.

A. Dapogny, K. Bailly, and S. Dubuisson, Multi-Output Random Forests for Facial Action Unit Detection, Automatic Face and Gesture Recognition, 2017.

A. Dapogny, K. Bailly, and S. Dubuisson, Dynamic facial expression recognition by joint static and multi-time gap transition classification, Automatic Face and Gesture Recognition, 2015.

A. Dapogny, K. Bailly, and S. Dubuisson, Pairwise Conditional Random Forests for Facial Expression Recognition, International Conference on Computer Vision, 2015.

A. Dapogny, A walk through randomness for face analysis in unconstrained environments, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01588960

T. Janssoone, Analyse de signaux sociaux multimodaux : application à la synthèse d'attitudes sociales chez un agent conversationnel animé, 2018.

C. Grossard, L. Chaby, S. Hun, H. Pellerin, J. Bourgeois et al., Children facial expression production : influence of age, gender, emotion subtype, elicitation condition and culture, Frontiers in Psychology, vol.9, p.446, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02422897

C. Grossard, . Grynspan, . Serret, . Al-jouen, . Bailly et al., Serious games to teach social interactions and emotions to individuals with autism spectrum disorders (ASD), Computers & Education, vol.113, pp.195-211, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01525828

T. Janssoone, . Clavel, . Bailly, and . Richard, Règles d'Associations Temporelles de signaux sociaux pour la synthèse d'Agents Conversationnels Animés, pp.512-517, 2017.

C. Grossard, . Hun, . Serret, P. Grynszpan, . Foulon et al., Rééducation de l'expression émotionnelle chez l'enfant avec trouble du spectre autistique grâce aux supports numériques : le projet JEMImE, vol.65, pp.21-32, 2017.

A. Dapogny, C. Grossard, S. Hun, S. Serret, J. Bourgeois et al., JEMImE : A Serious Game to Teach Children with ASD How to Adequately Produce Facial Expressions, Workshop on Face and Gesture Analysis for Health Informatics, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02076870

T. Janssoone, C. Clavel, K. Bailly, and G. Richard, Using temporal association rules for the synthesis of embodied conversational agents with a specific stance, International Conference on Intelligent Virtual Agents (IVA'2016)

S. Hun, S. Serret, C. Grossard, J. Bourgeois, O. Grynszpan et al., JEMImE, a serious game to teach emotional facial expressiveness for people with Autism Spectrum Disorders, International Meeting for Autism Research, 2017.

, Articles dans des revues internationales avec comité de lecture

A. Dapogny, K. Bailly, and . Séverine-dubuisson, Confidence-weighted local expression predictions for occlusion handling in expression recognition and action unit detection, International Journal of Computer Vision (IJCV), vol.126, issue.2-4, pp.255-271, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01509850

A. Dapogny, K. Bailly, and . Severine-dubuisson, Dynamic pose-robust facial expression recognition by multi-view pairwise conditional random forests, IEEE Transactions on Affective Computing, 2018.

A. Dapogny-et-kévin and . Bailly, Face alignment with cascaded semi-parametric deep greedy neural forests, Pattern Recognition Letters, vol.102, pp.75-81, 2018.

C. Grossard, L. Chaby, S. Hun, H. Pellerin, J. Bourgeois et al., Children facial expression production : influence of age, gender, emotion subtype, elicitation condition and culture, Frontiers in Psychology, vol.9, p.446, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02422897

L. Ouss, M. T. , L. Normand, K. Bailly, C. Marluce-leitgel-gille et al., Developmental trajectories of hand movements in typical infants and those at risk of developmental disorders : An observational study of kinematics during the first year of life, Frontiers in Psychology, vol.9, p.83, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-01807609

C. Grossard, O. Grynspan, S. Serret, A. Jouen, K. Bailly et al., Serious games to teach social interactions and emotions to individuals with autism spectrum disorders (asd), Computers & Education, vol.113, pp.195-211, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01525828

J. Nicolle, K. Bailly, and M. Chetouani, Real-time facial action unit intensity prediction with regularized metric learning, Image and Vision Computing, vol.52, pp.1-14, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01318177

T. Senechal, K. Bailly, and L. Prevost, Impact of action unit detection in automatic emotion recognition, Pattern Analysis and Applications, vol.17, issue.1, pp.51-67, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01279720

V. Rapp, K. Bailly, T. Senechal, and L. Prevost, Multi-kernel appearance model. Image and Vision Computing, vol.31, pp.542-554, 2013.

T. Senechal, V. Rapp, H. Salam, and R. Seguier, Kevin Bailly et Lionel Prevost : Facial action recognition combining heterogeneous features via multikernel learning, IEEE Transactions on Systems, Man, and Cybernetics -Part B, vol.42, issue.4, pp.993-1005, 2012.

K. Bailly and M. Milgram, Boosting feature selection for neural network based regression, Neural Networks, vol.22, issue.5-6, pp.748-756, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00731068

L. Ouss, M. T. , L. Normand, K. Bailly, C. Marluce-leitgel-gille et al., Developmental trajectories of hand movements in typical infants and those at risk of developmental disorders : An observational study of kinematics during the first year of life, Frontiers in Psychology, vol.9, p.83, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-01807609

C. Grossard, . Hun, . Serret, P. Grynszpan, . Foulon et al., Rééducation de l'expression émotionnelle chez l'enfant avec trouble du spectre autistique grâce aux supports numériques : le projet jemime, vol.65, pp.21-32, 2017.

T. Janssoone, C. Clavel, K. Bailly, and G. Richard, Règles d'associations temporelles de signaux sociaux pour la synthèse d'agents conversationnels animés, p.537, 2017.

, Conférences invitées internationales

K. Bailly, Random forests for facial expression analysis, SMART School on Computational Social and Behavioral Sciences, 2017.

, Articles de conférences internationales avec actes et comité de lecture

A. Dapogny, C. Grossard, S. Hun, S. Serret, J. Bourgeois et al., Jemime : A serious game to teach children with asd how to adequately produce facial expressions, IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2018.
URL : https://hal.archives-ouvertes.fr/hal-02076870

A. Dapogny and K. Bailly, Investigating deep neural forests for facial expression recognition, IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2018.

A. Vásquez and A. Dapogny, Kévin Bailly et Véronique Perdereau : Sequential recognition of in-hand object shape using a collection of neural forests, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.

A. Dapogny, K. Bailly, and . Séverine-dubuisson, Multi-output random forests for facial action unit detection, IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2017.

J. Aigrain, A. Dapogny, K. Bailly, and S. Dubuisson, Marcin Detyniecki et Mohamed Chetouani : On leveraging crowdsourced data for automatic perceived stress detection, ACM International Conference on Multimodal Interaction (ICMI), 2016.

T. Janssoone, C. Clavel, K. Bailly, and G. Richard, Using temporal association rules for the synthesis of embodied conversational agents with a specific stance, International Conference on Intelligent Virtual Agents (IVA), 2016.

A. Dapogny, K. Bailly, and . Séverine-dubuisson, Pairwise conditional random forests for facial expression recognition, IEEE International Conference on Computer Vision (ICCV), 2015.

J. Nicolle, K. Bailly, and M. Chetouani, Facial action unit intensity prediction via hard multi-task metric learning for kernel regression, IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2015.
URL : https://hal.archives-ouvertes.fr/hal-02423008

A. Dapogny, K. Bailly, and . Séverine-dubuisson, Dynamic facial expression recognition by joint static and multi-time gap transition classification, IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2015.

L. Zamuner, K. Bailly, and E. Bigorgne, A pose-adaptive constrained local model for accurate head pose tracking, International Conference on Pattern Recognition (ICPR), 2014.

J. Nicolle, K. Bailly, V. Rapp, and M. Chetouani, Locating facial landmarks with binary map cross-correlations, IEEE International Conference on Image Processing (ICIP), 2013. Articles de conférences internationales avec actes et
URL : https://hal.archives-ouvertes.fr/hal-02423139

K. Bailly and M. Milgram, Philippe Phothisane et Erwan Bigorgne : Learning global cost function for face alignment, International Conference on Pattern Recognition (ICPR), 2012.

J. Nicolle, V. Rapp, and K. Bailly, Lionel Prevost et Mohamed Chetouani : Robust continuous prediction of human emotions using multiscale dynamic cues, ACM international conference on Multimodal interaction (ICMI), 2012.

T. Senechal, V. Rapp, H. Salam, and R. Seguier, Kevin Bailly et Lionel Prevost : Combining aam coefficients with lgbp histograms in the multi-kernel svm framework to detect facial action units, IEEE International Conference on Automatic Face & Gesture Recognition(FG), 2011.

V. Rapp, T. Senechal, K. Bailly, and L. Prevost, Multiple kernel learning svm and statistical validation for facial landmark detection, IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2011.

T. Senechal, K. Bailly, and L. Prevost, Automatic facial action detection using histogram variation between emotional states, International Conference on Pattern Recognition (ICPR), 2010.

A. Mokadem and M. Charbit, Gérard Chollet et Kevin Bailly : Age regression based on local image features, Pacific-Rim Symposium on Image and Video Technology (PSIVT), 2010.

K. Bailly and M. Milgram, Head pan angle estimation by a nonlinear regression on selected features, IEEE International Conference on Image Processing, 2009.

K. Bailly, M. Milgram, and P. Phothisane, Head pose estimation by a stepwise nonlinear regression, International Conference on Computer Analysis of Images and Patterns (CAIP), 2009.

K. Bailly and M. Milgram, Bisar : Boosted input selection algorithm for regression, International Joint Conference on Neural Networks (IJCNN), 2009.

K. Bailly and M. Milgram, Recursive shape and pose determination using deformable model, Progress in Pattern Recognition, Image Analysis and Applications, 2008.

K. Bailly and M. Milgram, Head pose determination using synthetic images, International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS), 2008.

Y. Aidarouss, L. Sylvain, A. Gallou, R. Sattar, and . Seguier, Face Alignment using active appearance model optimized by simplex, International Conference on Computer Vision Theory and Applications (VISAPP), pp.231-234, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00205124

G. Ali and M. Choi, Boosted NNE collections for multicultural facial expression recognition, Pattern Recognition, vol.55, pp.14-27, 2016.

Z. Ambadar, J. F. Cohn, and L. I. Reed, All Smiles are Not Created Equal : Morphology and Timing of Smiles Perceived as Amused, Polite, and Embarrassed/Nervous, Journal of Nonverbal Behavior, vol.33, issue.1, pp.1573-3653, 2009.

N. Ambady and R. Rosenthal, Thin slices of expressive behavior as predictors of interpersonal consequences : A meta-analysis, Psychological Bulletin, vol.111, issue.2, pp.33-2909, 1992.

A. Ashraf, S. Lucey, J. F. Cohn, T. Chen, Z. Ambadar et al., The painful face -Pain expression recognition using active appearance models, Image and Vision Computing, vol.27, issue.12, pp.1788-1796, 2009.

A. Asthana, S. Zafeiriou, and S. Cheng, Pantic : Robust Discriminative Response Map Fitting with Constrained Local Models, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3444-3451, 2013.

A. Asthana, S. Zafeiriou, S. Cheng, and M. Pantic, Incremental Face Alignment in the Wild, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1859-1866, 2014.

T. Baltru?aitis, M. Mahmoud, and P. Robinson, Cross-dataset learning and personspecific normalisation for automatic Action Unit detection, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol.06, pp.1-6, 2015.

T. Baltru?aitis, D. Mcduff, N. Banda, M. Mahmoud, R. Kaliouby et al., Real-time inference of mental states from facial expressions and upper body gestures, Face and Gesture, pp.909-914, 2011.

M. S. Bartlett, G. Littlewort, I. Fasel, and J. R. , Movellan : Real Time Face Detection and Facial Expression Recognition : Development and Applications to Human Computer Interaction, Conference on Computer Vision and Pattern Recognition Workshop, vol.5, pp.53-53, 2003.

M. S. Bartlett, G. Littlewort, M. Frank, C. Lainscsek, I. Fasel et al., Recognizing facial expression : Machine learning and application to spontaneous behavior, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol.2, pp.568-573, 2005.

M. S. Bartlett, G. Littlewort, G. Mark, C. Frank, . Lainscsek et al., Automatic recognition of facial actions in spontaneous expressions, Journal of multimedia, vol.1, issue.6, pp.22-35, 2006.

J. J. Bazzo and M. V. Lamar, Recognizing facial actions using Gabor wavelets with neutral face average difference, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.505-510, 2004.

N. Peter, D. W. Belhumeur, D. J. Jacobs, N. Kriegman, and . Kumar, Localizing Parts of Faces Using a Consensus of Exemplars, The 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

A. Ben and -. Noble, Kernel methods for predicting protein-protein interactions, Bioinformatics, vol.21, issue.suppl_1, pp.38-46, 2005.

L. Breiman, Bagging predictors, Machine Learning, vol.24, pp.1573-0565, 1996.

L. Breiman, Random Forests. Machine Learning, vol.45, pp.5-32, 2001.

N. Brunet, F. Perez, F. De-la, and T. , Learning good features for Active Shape Models, IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.206-211, 2009.

X. P. Burgos-artizzu, P. Perona, and P. Dollár, Robust Face Landmark Estimation under Occlusion, 2013 IEEE International Conference on Computer Vision, pp.1513-1520, 2013.

X. Cao, Y. Wei, F. Wen, and J. Sun, Face alignment by Explicit Shape Regression, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2887-2894, 2012.

S. W. Chew, P. Lucey, S. Lucey, J. Saragih, J. F. Cohn et al., the Pursuit of Effective Affective Computing : The Relationship Between Features and Registration. IEEE Transactions on Systems, Man, and Cybernetics, vol.42, pp.1006-1016, 2012.

S. W. Chew, P. Lucey, S. Lucey, J. Saragih, J. F. Cohn et al., Personindependent facial expression detection using Constrained Local Models, Face and Gesture 2011, pp.915-920, 2011.

W. Chu, F. De-la-torre, and J. F. Cohn, Selective Transfer Machine for Personalized Facial Action Unit Detection, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3515-3522, 2013.

. Chao-fa, . Chuang, Y. Frank, and . Shih, Recognizing facial action units using independent component analysis and support vector machine, Pattern Recognition, vol.39, issue.9, pp.31-3203, 2006.

J. A. Coan and J. B. John, Allen, éditeurs. Handbook of Emotion Elicitation and Assessment. Handbook of emotion elicitation and assessment, 2007.

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.

T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, Active Shape Models-Their Training and Application. Computer Vision and Image Understanding, vol.61, pp.38-59, 1995.

S. F. Cotter, Sparse Representation for accurate classification of corrupted and occluded facial expressions, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.838-841, 2010.

M. Dahmane and J. Meunier, Emotion recognition using dynamic grid-based HoG features, Face and Gesture, pp.884-888, 2011.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol.1, pp.886-893, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

A. Dapogny, K. Bailly, and S. Dubuisson, Pairwise Conditional Random Forests for Facial Expression Recognition, 2015 IEEE International Conference on Computer Vision (ICCV), pp.3783-3791, 2015.

A. Dapogny, K. Bailly, and S. Dubuisson, Multi-Output Random Forests for Facial Action Unit Detection, 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), pp.135-140, 2017.

K. Dapogny, S. Bailly, and . Dubuisson, Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests, IEEE Transactions on Affective Computing, pp.1-1, 2018.

A. Dapogny, C. Grossard, S. Hun, S. Serret, J. Bourgeois et al., JEMImE -A Serious Game to Teach Children with ASD How to Adequately Produce Facial Expressions, 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp.723-730, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02076870

A. Dapogny, A Walk through Randomness for Face Analysis in Unconstrained Environments, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01588960

A. Dapogny-et-kévin and . Bailly, Face alignment with cascaded semi-parametric deep greedy neural forests, Pattern Recognition Letters, vol.102, pp.75-81, 2018.

A. Dapogny, K. Bailly, and . Séverine-dubuisson, Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit Detection, International Journal of Computer Vision, vol.126, issue.2, pp.255-271, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01509850

A. Dhall, A. Asthana, R. Goecke, and T. Gedeon, Emotion recognition using PHOG and LPQ features, Face and Gesture, pp.878-883, 2011.

A. Dhall, R. Goecke, S. Lucey, and T. Gedeon, Static facial expression analysis in tough conditions : Data, evaluation protocol and benchmark, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp.2106-2112, 2011.

A. Dhall, O. V. Murthy, R. Goecke, J. Joshi, and T. Gedeon, Video and Image Based Emotion Recognition Challenges in the Wild : EmotiW, Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, ICMI '15, pp.423-426, 2015.

H. Dibeklio?lu, A. A. Salah, and T. Gevers, Are You Really Smiling at Me ? Spontaneous versus Posed Enjoyment Smiles, Computer Vision -ECCV 2012, pp.525-538, 2012.

G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman, and T. J. Sejnowski, Classifying facial actions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.10, pp.974-989, 1999.

X. Dong, Y. Yan, W. Ouyang, and Y. Yang, Style Aggregated Network for Facial Landmark Detection, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

R. Donner, M. Reiter, G. Langs, P. Peloschek, and . Bischof, Fast Active Appearance Model Search Using Canonical Correlation Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1690-1694, 2006.

P. Ekman, V. Wallace, and . Friesen, Constants across cultures in the face and emotion, Journal of personality and social psychology, vol.17, issue.2, p.124, 1971.

P. Ekman, V. Wallace, and . Friesen, Measuring facial movement. Environmental psychology and nonverbal behavior, vol.1, pp.56-75, 1976.

S. Eleftheriadis, O. Rudovic, and M. Pantic, Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition, IEEE Transactions on Image Processing, vol.24, issue.1, pp.189-204, 2015.

T. Evgeniou and M. Pontil, Regularized Multi-task Learning, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '04, pp.109-117, 2004.

Y. Fan, X. Lu, D. Li, and Y. Liu, Video-based Emotion Recognition Using CNN-RNN and C3D Hybrid Networks, Proceedings of the 18th ACM International Conference on Multimodal Interaction, ICMI 2016, pp.445-450, 2016.

X. Gao, Y. Su, X. Li, and D. Tao, A Review of Active Appearance Models, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.40, pp.145-158, 2010.

C. Garcia and M. Delakis, Convolutional face finder : A neural architecture for fast and robust face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1408-1423, 2004.

G. Ghiasi and C. C. Fowlkes, Occlusion Coherence : Localizing Occluded Faces with a Hierarchical Deformable Part Model, pp.1899-1906, 2014.

J. M. Girard, J. F. Cohn, F. De-la, and T. , Estimating smile intensity : A better way, Pattern recognition letters, vol.66, pp.13-21, 2015.

. Mehmet-gönen-et-ethem-alpayd?n, Multiple Kernel Learning Algorithms, Journal of Machine Learning Research, vol.12, pp.2211-2268, 2011.

I. Gonzalez, H. Sahli, V. Enescu, and W. Verhelst, Context-Independent Facial Action Unit Recognition Using Shape and Gabor Phase Information, Affective Computing and Intelligent Interaction, pp.548-557, 2011.

R. Gross, I. Matthews, and S. Baker, Generic vs. person specific active appearance models, Image and Vision Computing, vol.23, issue.12, pp.1080-1093, 2005.

C. Grossard, L. Chaby, S. Hun, H. Pellerin, J. Bourgeois et al., Ouriel Grynszpan et David Cohen : Children Facial Expression Production : Influence of Age, Gender, Emotion Subtype, Elicitation Condition and Culture, Frontiers in Psychology, vol.9, 2018.

H. Gu and Q. Ji, Facial Event Classification with Task Oriented Dynamic Bayesian Network, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.02, pp.870-875, 2004.

A. Gudi, H. E. Tasli, T. M. Uyl, and A. Maroulis, Deep learning based FACS Action Unit occurrence and intensity estimation, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol.06, pp.1-5, 2015.

-. Mathieu-guillame, J. L. Bert, and . Crowley, Learning Temporal Association Rules on Symbolic Time Sequences. In Asian Conference on Machine Learning, pp.159-174, 2012.

E. Rain, E. Haamer, I. Rusadze, T. Lüsi, S. Ahmed et al., Review on Emotion Recognition Databases. Human-Robot Interaction-Theory and Application. IntechOpen, 2018.

B. Hasani and M. H. Mahoor, Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.2278-2288, 2017.

T. Hassner, S. Harel, E. Paz, and R. Enbar, Effective face frontalization in unconstrained images, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4295-4304, 2015.

M. Hayat, M. Bennamoun, and A. El-sallam, Evaluation of Spatiotemporal Detectors and Descriptors for Facial Expression Recognition, 2012 5th International Conference on Human System Interactions, pp.43-47, 2012.

L. He, C. Zou, L. Zhao, and D. Hu, An Enhanced LBP Feature Based on Facial Expression Recognition, IEEE Engineering in Medicine and Biology 27th Annual Conference, pp.3300-3303, 2005.

Y. Hu, Z. Zeng, L. Yin, X. Wei, X. Zhou et al., Multi-view facial expression recognition, 8th IEEE International Conference on Automatic Face Gesture Recognition, pp.1-6, 2008.

X. Huang and G. Zhao, Wenming Zheng et Matti Pietikäinen : Towards a dynamic expression recognition system under facial occlusion, Pattern Recognition Letters, vol.33, issue.16, pp.2181-2191, 2012.

S. Jaiswal and M. Valstar, Deep learning the dynamic appearance and shape of facial action units, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.1-8, 2016.

L. A. Jeni, J. M. Girard, J. F. Cohn, F. De-la, and T. , Continuous AU intensity estimation using localized, sparse facial feature space, 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp.1-7, 2013.

O. Jesorsky, K. J. Kirchberg, and R. W. Frischholz, Robust Face Detection Using the Hausdorff Distance. In Audio-and Video-Based Biometric Person Authentication, Lecture Notes in Computer Science, pp.90-95, 2001.

H. Jung, S. Lee, J. Yim, S. Park, and J. Kim, Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition, 2015 IEEE International Conference on Computer Vision (ICCV), pp.2983-2991, 2015.

H. Jung, S. Lee, S. Park, I. Lee, C. Ahn et al., Deep Temporal Appearance-Geometry Network for Facial Expression Recognition, 2015.

T. Kanade, J. F. Cohn, and Y. Tian, Comprehensive database for facial expression analysis, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp.46-53, 2000.

D. Keltner, Signs of appeasement : Evidence for the distinct displays of embarrassment, amusement, and shame, Journal of Personality and Social Psychology, vol.68, issue.3, pp.22-3514, 1995.

M. Khademi and L. P. Morency, Relative facial action unit detection, IEEE Winter Conference on Applications of Computer Vision, pp.1090-1095, 2014.

P. Khorrami, T. L. Paine, and T. S. Huang, Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition ?, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp.19-27, 2015.

B. Kim, H. Lee, J. Roh, and S. Lee, Hierarchical Committee of Deep CNNs with Exponentially-Weighted Decision Fusion for Static Facial Expression Recognition, Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, ICMI '15, pp.427-434, 2015.

P. Kontschieder, M. Fiterau, A. Criminisi, and S. R. Bulò, Deep Neural Decision Forests, 2015 IEEE International Conference on Computer Vision (ICCV), pp.1467-1475, 2015.

I. Kotsia, I. Buciu, and I. Pitas, An analysis of facial expression recognition under partial facial image occlusion, Image and Vision Computing, vol.26, issue.7, pp.1052-1067, 2008.

M. Kowalski, J. Naruniec, and T. Trzcinski, Deep Alignment Network : A Convolutional Neural Network for Robust Face Alignment, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.2034-2043, 2017.

B. Lakshminarayanan, M. Daniel, Y. Roy, ;. Z. Teh, M. Ghahramani et al., Mondrian Forests : Efficient Online Random Forests, éditeurs : Advances in Neural Information Processing Systems, vol.27, pp.3140-3148, 2014.

J. Vuong-le, Z. Brandt, L. Lin, . Bourdev, S. Thomas et al., European Conference on Computer Vision, pp.679-692, 2012.

Y. Li, J. Chen, Y. Zhao, and Q. Ji, Data-Free Prior Model for Facial Action Unit Recognition, IEEE Transactions on Affective Computing, vol.4, issue.2, pp.127-141, 2013.

Y. Li, S. Liu, J. Yang, and M. H. Yang, Generative Face Completion, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5892-5900, 2017.

R. Dieu-linh-tran and . Walecki, Ognjen (Oggi) Rudovic, Stefanos Eleftheriadis, Bjorn Schuller et Maja Pantic : DeepCoder : Semi-Parametric Variational Autoencoders for Automatic Facial Action Coding, 2017 IEEE International Conference on Computer Vision (ICCV), pp.3190-3199, 2017.

G. Littlewort, J. Whitehill, T. Wu, I. Fasel, M. Frank et al., The computer expression recognition toolbox (CERT). In Face and Gesture, pp.298-305, 2011.

G. Littlewort, M. S. Bartlett, I. Fasel, J. Susskind, and J. Movellan, Dynamics of facial expression extracted automatically from video, Image and Vision Computing, vol.24, issue.6, pp.262-8856, 2006.

M. Liu, S. Li, S. Shan, and X. Chen, AU-aware Deep Networks for facial expression recognition, 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp.1-6, 2013.

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, pp.1805-1812, 2014.

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, vol.2, pp.1150-1157, 1999.

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar et al., The Extended Cohn-Kanade Dataset (CK+) : A complete dataset for action unit and emotionspecified expression, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition -Workshops, pp.94-101, 2010.

M. J. Lyons, J. Budynek, and S. Akamatsu, Automatic classification of single facial images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.12, pp.1357-1362, 1999.

B. Martinez, M. F. Valstar, B. Jiang, and M. Pantic, Automatic Analysis of Facial Actions : A Survey. IEEE Transactions on Affective Computing, pp.1-1, 2017.

B. Martinez and F. Michel, Valstar : L2,1-based regression and prediction accumulation across views for robust facial landmark detection, Image and Vision Computing, vol.47, pp.36-44, 2016.

I. Matthews and S. Baker, Active Appearance Models Revisited, International Journal of Computer Vision, vol.60, issue.2, pp.1573-1405, 2004.

S. M. Mavadati, M. H. Mahoor, K. Bartlett, P. Trinh, and J. F. Cohn, DISFA A Spontaneous Facial Action Intensity Database. IEEE Transactions on Affective Computing, vol.4, pp.151-160, 2013.

M. K. Abd-el-meguid and M. D. Levine, Fully automated recognition of spontaneous facial expressions in videos using random forest classifiers, IEEE Transactions on Affective Computing, vol.5, issue.2, pp.141-154, 2014.

H. Meng, B. Romera-paredes, and N. Bianchi-berthouze, Emotion recognition by two view SVM_2K classifier on dynamic facial expression features, Face and Gesture, pp.854-859, 2011.

S. Milborrow and F. Nicolls, Locating Facial Features with an Extended Active Shape Model, Computer Vision -ECCV, pp.504-513, 2008.

Z. Ming, A. Bugeau, J. Rouas, and T. Shochi, Facial Action Units intensity estimation by the fusion of features with multi-kernel Support Vector Machine, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol.06, pp.1-6, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01126775

S. Moore and R. Bowden, Local binary patterns for multi-view facial expression recognition, Computer Vision and Image Understanding, vol.115, issue.4, pp.541-558, 2011.

S. Moore and R. Bowden, The effects of Pose on Facial Expression Recognition, Proceedings of the British Machine Vision Conference, pp.79-80, 2009.

H. Ng, V. D. Nguyen, V. Vonikakis, and S. Winkler, Deep Learning for Emotion Recognition on Small Datasets Using Transfer Learning, Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, ICMI '15, pp.443-449, 2015.

J. Nicolle, K. Bailly, and M. Chetouani, Facial Action Unit intensity prediction via Hard Multi-Task Metric Learning for Kernel Regression, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol.06, pp.1-6, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02423008

J. Nicolle, Using Hard Multi-Task Metric Learning for Kernel Regression, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01365433

J. Nicolle, K. Bailly, and M. Chetouani, Real-time facial action unit intensity prediction with regularized metric learning, Image and Vision Computing, vol.52, pp.1-14, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01318177

J. Nicolle, V. Rapp, and K. Bailly, Lionel Prevost et Mohamed Chetouani : Robust Continuous Prediction of Human Emotions Using Multiscale Dynamic Cues, Proceedings of the 14th ACM International Conference on Multimodal Interaction, ICMI '12, pp.501-508, 2012.

M. Michael-m-nordstrøm, J. Larsen, . Sierakowski, B. Mikkel, and . Stegmann, The IMM face database. environment, vol.22, pp.1319-1331, 2003.

T. Ojala, M. Pietikäinen, and D. Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, vol.29, issue.1, pp.51-59, 1996.

M. Osadchy, Y. Le-cun, and M. L. Miller, Synergistic Face Detection and Pose Estimation with Energy-Based Models, Journal of Machine Learning Research, vol.8, pp.1197-1215, 2007.

E. Owusu, Y. Zhan, . Qi-rong, and . Mao, A neural-AdaBoost based facial expression recognition system, Expert Systems with Applications, vol.41, issue.7, pp.3383-3390, 2014.

M. Pantic, M. Valstar, R. Rademaker, and L. Maat, Web-based database for facial expression analysis, IEEE International Conference on Multimedia and Expo, vol.5, 2005.

O. M. Parkhi, A. Vedaldi, and A. Zisserman, Deep Face Recognition, British Machine Vision Conference, 2015.

J. C. Platt, Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, Advances in Large Margin Classifiers, pp.61-74, 1999.

M. Ranzato, J. Susskind, V. Mnih, and G. Hinton, On deep generative models with applications to recognition, CVPR 2011, pp.2857-2864, 2011.

V. Rapp, K. Bailly, T. Senechal, and L. Prevost, Multi-Kernel Appearance Model. Image and Vision Computing, vol.31, pp.542-554, 2013.

S. Ren, X. Cao, Y. Wei, and J. Sun, Face Alignment at 3000 FPS via Regressing Local Binary Features, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1685-1692, 2014.

S. Romdhani and T. , Vetter : Efficient, robust and accurate fitting of a 3D morphable model, Proceedings Ninth IEEE International Conference on Computer Vision, vol.1, pp.59-66, 2003.

O. Rudovic, M. Pantic, and I. Patras, Coupled Gaussian processes for pose-invariant facial expression recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, pp.1357-1369, 2013.

A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof, On-line Random Forests, IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.1393-1400, 2009.

J. M. Saragih, S. Lucey, and J. F. Cohn, Deformable Model Fitting by Regularized Landmark Mean-Shift, International Journal of Computer Vision, vol.91, issue.2, pp.1573-1405, 2011.

E. Sariyanidi, H. Gunes, and A. Cavallaro, Automatic Analysis of Facial Affect : A Survey of Registration, Representation, and Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.6, pp.1113-1133, 2015.

A. Savran, N. Alyüz, H. Dibeklio?lu, O. Çeliktutan, and B. Gökberk, Bülent Sankur et Lale Akarun : Bosphorus Database for 3D Face Analysis, Biometrics and Identity Management, pp.47-56, 2008.

B. Schuller, M. Valster, F. Eyben, R. Cowie, and M. Pantic, AVEC 2012 : The Continuous Audio/Visual Emotion Challenge, Proceedings of the 14th ACM International Conference on Multimodal Interaction, ICMI '12, pp.449-456, 2012.

T. Senechal, K. Bailly, and L. Prevost, Automatic Facial Action Detection Using Histogram Variation Between Emotional States, 20th International Conference on Pattern Recognition, pp.3752-3755, 2010.

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, vol.42, pp.993-1005, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00731864

T. Senechal, Ce Que Le Visage Révèle : Analyse Des Mouvements Faciaux Pour l'interprétation Émotionnelle, 2011.

C. Shan, P. W. Shaogang-gong, and . Mcowan, Robust facial expression recognition using local binary patterns, IEEE International Conference on Image Processing, vol.2, pp.370-373, 2005.

C. Shan, S. Gong, W. Peter, and . Mcowan, Facial expression recognition based on Local Binary Patterns : A comprehensive study. Image and Vision Computing, vol.27, pp.803-816, 2009.

J. Shen, S. Zafeiriou, G. G. Chrysos, J. Kossaifi, G. Tzimiropoulos et al., The First Facial Landmark Tracking in-the-Wild Challenge : Benchmark and Results, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp.1003-1011, 2015.

S. Shojaeilangari, W. Yau, J. Li, and E. Teoh, Multiscale analysis of local phase and local orientation for dynamic facial expression recognition, Journal ISSN, vol.1, issue.1, 2014.

T. Sim, S. Baker, and M. Bsat, The CMU Pose, Illumination, and Expression (PIE) Database of Human Faces, 2001.

R. Srivastava, S. Roy, S. Yan, and T. Sim, Accumulated motion images for facial expression recognition in videos, Face and Gesture, pp.903-908, 2011.

Y. Sun, X. Wang, and X. Tang, Deep Convolutional Network Cascade for Facial Point Detection, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3476-3483, 2013.

U. Tariq, K. H. Lin, Z. Li, X. Zhou, Z. Wang et al., Emotion recognition from an ensemble of features, Face and Gesture, pp.872-877, 2011.

U. Tariq, J. Yang, and T. S. Huang, Multi-view Facial Expression Recognition Analysis with Generic Sparse Coding Feature, Computer Vision -ECCV 2012. Workshops and Demonstrations, pp.578-588

. Springer, , 2012.

R. Tibshirani, Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.58, issue.1, pp.35-9246, 1996.

Y. Tong, W. Liao, and Q. Ji, Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.10, pp.1683-1699, 2007.

G. Trigeorgis, P. Snape, and M. A. Nicolaou, Epameinondas Antonakos et Stefanos Zafeiriou : Mnemonic Descent Method : A Recurrent Process Applied for End-To-End Face Alignment, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.4177-4187, 2016.

G. Tzimiropoulos, Project-Out Cascaded Regression with an application to face alignment, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3659-3667, 2015.

G. Tzimiropoulos and M. Pantic, Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1851-1858, 2014.

G. Tzimiropoulos and J. Alabort-i-medina, Stefanos Zafeiriou et Maja Pantic : Generic Active Appearance Models Revisited, Computer Vision -ACCV 2012, pp.650-663, 2012.

M. Valstar, M. Pantic, and I. Patras, Motion history for facial action detection in video, 2004 IEEE International Conference on Systems, Man and Cybernetics, vol.1, pp.635-640, 2004.

M. F. Valstar, T. Almaev, J. M. Girard, G. Mckeown, M. Mehu et al., FERA 2015 -second Facial Expression Recognition and Analysis challenge, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol.06, pp.1-8, 2015.

M. F. Valstar, B. Jiang, M. Mehu, M. Pantic, and K. Scherer, The first facial expression recognition and analysis challenge, Face and Gesture, pp.921-926, 2011.

M. F. Valstar, M. Mehu, B. Jiang, M. Pantic, and K. Scherer, Meta-Analysis of the First Facial Expression Recognition Challenge, IEEE Transactions on Systems, Man, and Cybernetics, vol.42, issue.4, pp.966-979, 2012.

R. L. Vieriu, S. Tulyakov, S. Semeniuta, E. Sangineto, and N. Sebe, Facial expression recognition under a wide range of head poses, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol.1, pp.1-7, 2015.

P. Viola and M. Jones, 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, vol.1, 2001.

F. Wallhoff, Database with Facial Expressions and Emotions from Technical University of Munich (FEEDTUM), 2006.

J. Wang, S. Wang, and Q. Ji, Early Facial Expression Recognition Using Hidden Markov Models, 22nd International Conference on Pattern Recognition, pp.4594-4599, 2014.

Z. Wang, S. Wang, and Q. Ji, Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3422-3429, 2013.

Z. Wang and . Qiuqi-ruan-et-gaoyun-an, Facial expression recognition using sparse local Fisher discriminant analysis, Neurocomputing, vol.174, pp.756-766, 2016.

Q. Kilian, G. Weinberger, and . Tesauro, Metric Learning for Kernel Regression, Artificial Intelligence and Statistics, pp.612-619, 2007.

P. Werner, F. Saxen, and A. Al-hamadi, Handling Data Imbalance in Automatic Facial Action Intensity Estimation, Proceedings of the British Machine Vision Conference (BMVC), 2015.

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.2106-2111, 2009.

J. Whitehill and C. W. Omlin, Haar features for FACS AU recognition, 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp.5-101, 2006.

T. Wu, N. J. Butko, P. Ruvolo, J. Whitehill, M. S. Bartlett et al., Movellan : Action unit recognition transfer across datasets, Face and Gesture, pp.889-896, 2011.

W. Wu, C. Qian, S. Yang, Q. Wang, Y. Cai et al., Look at Boundary : A Boundary-Aware Face Alignment Algorithm, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

X. Xiong, F. De-la, and T. , Supervised Descent Method and Its Applications to Face Alignment, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.532-539, 2013.

H. Yang, X. Jia, C. Loy, and P. Robinson, An Empirical Study of Recent Face Alignment Methods, 2015.

P. Yang, Q. Liu, and D. N. Metaxas, Exploring facial expressions with compositional features, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2638-2644, 2010.

P. Yang, Q. Liu, and D. N. Metaxas, Boosting encoded dynamic features for facial expression recognition, Pattern Recognition Letters, vol.30, issue.2, pp.132-139, 2009.

L. Yin, X. Chen, Y. Sun, T. Worm, and M. Reale, A high-resolution 3D dynamic facial expression database, 8th IEEE International Conference on Automatic Face Gesture Recognition, pp.1-6, 2008.

A. A. Aliaa, . Youssif, A. A. Wesam, and . Asker, Automatic Facial Expression Recognition System Based on Geometric and Appearance Features, Computer and Information Science, vol.4, issue.2, p.115, 2011.

Z. Yu and C. Zhang, Image Based Static Facial Expression Recognition with Multiple Deep Network Learning, Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, ICMI '15, pp.435-442, 2015.

S. Zafeiriou, D. Kollias, M. A. Nicolaou, A. Papaioannou, G. Zhao et al., Aff-Wild : Valence and Arousal In-the-Wild Challenge, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.1980-1987, 2017.

S. Zafeiriou, C. Zhang, and Z. Zhang, A survey on face detection in the wild : Past, present and future. Computer Vision and Image Understanding, vol.138, pp.1-24, 2015.

J. Zeng, W. Chu, F. De-la-torre, J. F. Cohn, and Z. Xiong, Confidence Preserving Machine for Facial Action Unit Detection, IEEE Transactions on Image Processing, vol.25, issue.10, pp.4753-4767, 2016.

J. Zhang, S. Shan, M. Kan, and X. Chen, Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment, Computer Vision -ECCV 2014, pp.1-16, 2014.

L. Zhang, D. Tjondronegoro, and V. Chandran, Random Gabor based templates for facial expression recognition in images with facial occlusion, Neurocomputing, vol.145, pp.451-464, 2014.

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.692-706, 2013.

Y. Zhang-et-qiang and J. , 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.

Z. Zhang, P. Luo, C. C. Loy, and X. Tang, Learning Deep Representation for Face Alignment with Auxiliary Attributes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.5, pp.918-930, 2016.

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.

K. Zhao, W. S. Chu, and H. Zhang, Deep Region and Multi-label Learning for Facial Action Unit Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3391-3399, 2016.

K. Zhao, W. Chu, F. De-la-torre, J. F. Cohn, and H. Zhang, Joint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition, IEEE Transactions on Image Processing, vol.25, issue.8, pp.1941-1983, 2016.

W. Zheng, H. Tang, Z. Lin, and T. S. Huang, Emotion Recognition from Arbitrary View Facial Images, Computer Vision -ECCV 2010, pp.490-503, 2010.

Z. Xiangyu-zhu, J. Lei, D. Yan, S. Z. Yi, and . Li, High-fidelity Pose and Expression Normalization for face recognition in the wild, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.787-796, 2015.

Y. Zhu, L. C. Silva, and C. C. Ko, Using moment invariants and HMM in facial expression recognition, Pattern Recognition Letters, vol.23, issue.1, pp.83-91, 2002.