I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning, vol.1, 2016.

H. Jung, S. Lee, J. Yim, S. Park, and J. Kim, Joint fine-tuning in deep neural networks for facial expression recognition, Computer Vision (ICCV), 2015 IEEE International Conference on, pp.2983-2991, 2015.

P. Liu, S. Han, Z. Meng, and Y. Tong, Facial expression recognition via a boosted deep belief network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1805-1812, 2014.

X. Zhao, X. Liang, L. Liu, T. Li, Y. Han et al., Peak-piloted deep network for facial expression recognition, pp.425-442, 2016.

H. Ding, S. K. Zhou, and R. Chellappa, Facenet2expnet: Regularizing a deep face recognition net for expression recognition, Automatic Face & Gesture Recognition (FG 2017, pp.118-126, 2017.

Z. Meng, P. Liu, J. Cai, S. Han, and Y. Tong, Identity-aware convolutional neural network for facial expression recognition, Automatic Face & Gesture Recognition (FG 2017, pp.558-565, 2017.

G. Papandreou, L. Chen, K. Murphy, and A. L. Yuille, Weakly-and semi-supervised learning of a dcnn for semantic image segmentation, 2015.

D. Lee, Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, vol.3, p.2, 2013.

S. Hong, H. Noh, and B. Han, Decoupled deep neural network for semi-supervised semantic segmentation, Advances in neural information processing systems, pp.1495-1503, 2015.

Z. Zhou, A brief introduction to weakly supervised learning, National Science Review, 2017.

O. M. Parkhi, A. Vedaldi, and A. Zisserman, Deep face recognition, BMVC, vol.1, p.6, 2015.

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, pp.1113-1133, 2015.

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, Learning and transferring mid-level image representations using convolutional neural networks, Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference, pp.1717-1724, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00911179

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, pp.443-449, 2015.

A. Coates and A. Y. Ng, Learning feature representations with kmeans, Neural networks: Tricks of the trade, pp.561-580, 2012.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010.

J. Zhao, M. Mathieu, R. Goroshin, and Y. Lecun, Stacked what-where autoencoders, 2015.

A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, Advances in Neural Information Processing Systems, pp.3546-3554, 2015.

A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, 2015.

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford et al., Improved techniques for training gans, Advances in Neural Information Processing Systems, pp.2234-2242, 2016.

C. Rosenberg, M. Hebert, and H. Schneiderman, Semi-supervised self-training of object detection models, 2005.

S. Novotney, R. Schwartz, and J. Ma, Unsupervised acoustic and language model training with small amounts of labelled data, Acoustics, Speech and Signal Processing, pp.4297-4300, 2009.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2818-2826, 2016.

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 emotion-specified expression, Computer Vision and Pattern Recognition Workshops (CVPRW), pp.94-101, 2010.

O. Langner, R. Dotsch, G. Bijlstra, D. H. Wigboldus, S. T. Hawk et al., Presentation and validation of the radboud faces database, Cognition and emotion, vol.24, issue.8, pp.1377-1388, 2010.

M. Minear and D. C. Park, A lifespan database of adult facial stimuli, Behavior Research Methods, Instruments, & Computers, vol.36, issue.4, pp.630-633, 2004.

I. J. Goodfellow, D. Erhan, and P. L. Carrier, Challenges in representation learning: A report on three machine learning contests, International Conference on Neural Information Processing, pp.117-124, 2013.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

M. Liu, S. Shan, R. Wang, and X. Chen, Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition, Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference, pp.1749-1756, 2014.

K. Sikka, G. Sharma, and M. Bartlett, Lomo: Latent ordinal model for facial analysis in videos, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5580-5589, 2016.

B. Jiang and K. Jia, Robust facial expression recognition algorithm based on local metric learning, Journal of Electronic Imaging, vol.25, issue.1, p.13022, 2016.

B. Wu and C. Lin, Adaptive feature mapping for customizing deep learning based facial expression recognition model, IEEE Access, vol.6, pp.12-451, 2018.

W. Sun, H. Zhao, and Z. Jin, An efficient unconstrained facial expression recognition algorithm based on stack binarized auto-encoders and binarized neural networks, Neurocomputing, vol.267, pp.385-395, 2017.

Y. Zhou and B. E. Shi, Action unit selective feature maps in deep networks for facial expression recognition, 2017 International Joint Conference on, pp.2031-2038, 2017.

P. Carcagnì, M. Coco, M. Leo, and C. Distante, Facial expression recognition and histograms of oriented gradients: a comprehensive study, SpringerPlus, vol.4, issue.1, p.645, 2015.

G. Zeng, J. Zhou, X. Jia, W. Xie, and L. Shen, Hand-crafted feature guided deep learning for facial expression recognition, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG, pp.423-430, 2018.

Y. Guo, D. Tao, J. Yu, H. Xiong, Y. Li et al., Deep neural networks with relativity learning for facial expression recognition, 2016 IEEE International Conference on Multimedia & Expo Workshops, pp.1-6, 2016.

C. Pramerdorfer and M. Kampel, Facial expression recognition using convolutional neural networks: state of the art, 2016.

B. Kim, S. Dong, J. Roh, G. Kim, and S. Lee, Fusing aligned and non-aligned face information for automatic affect recognition in the wild: a deep learning approach, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.48-57, 2016.

Y. Gan, J. Chen, and L. Xu, Facial expression recognition boosted by soft label with a diverse ensemble, Pattern Recognition Letters, vol.125, pp.105-112, 2019.