B. Amos, B. Ludwiczuk, and M. Satyanarayanan, Openface: A general-purpose face recognition library with mobile applications, CMU School of Computer Science, vol.6, issue.2, 2016.

K. Anuharshini, M. Sivaranjani, . Sowmiya, B. Mahesh, and . Geethanjali, Analyzing the Music Perception Based on Physiological Signals, 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp.411-416, 2019.

M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein generative adversarial networks, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.214-223, 2017.

P. Arlotto, M. Grimaldi, R. Naeck, and J. Ginoux, An ultrasonic contactless sensor for breathing monitoring, Sensors, vol.14, pp.15371-15386, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01058877

K. Pradeep-raj, U. Babu, and . Lahiri, Classification approach for understanding implications of emotions using eye-gaze, Journal of Ambient Intelligence and Humanized Computing, pp.1-13, 2019.

A. Bizzego, A. Azhari, N. Campostrini, A. Truzzi, L. Y. Ng et al., 2020. Strangers, Friends, and Lovers Show Different Physiological Synchrony in Different Emotional States, Behavioral Sciences, vol.10, p.11, 2020.

A. Chatterjee, K. Nath-narahari, M. Joshi, and P. Agrawal, SemEval-2019 task 3: EmoContext contextual emotion detection in text, Proceedings of the 13th International Workshop on Semantic Evaluation, pp.39-48, 2019.

. Ja-domínguez-jiménez, J. C. Campo-landines, . Martínez-santos, S. H. Delahoz, and . Contreras-ortiz, A machine learning model for emotion recognition from physiological signals, Biomedical Signal Processing and Control, vol.55, p.101646, 2020.

P. Ekman, An argument for basic emotions, Cognition & emotion, vol.6, pp.169-200, 1992.

. Rosenberg-ekman, What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS), 1997.

F. Eyben, F. Weninger, F. Gross, and B. Schuller, Recent developments in opensmile, the munich open-source multimedia feature extractor, Proceedings of the 21st ACM international conference on Multimedia, pp.835-838, 2013.

I. Goodfellow, Y. Bengio, and C. Aaron, Deep learning, 2016.

C. Gouveia, A. Tomé, F. Barros, C. Sandra, J. Soares et al., Study on the usage feasibility of continuous-wave radar for emotion recognition, Biomedical Signal Processing and Control, vol.58, p.101835, 2020.

A. Rabab, . Hameed, K. Mohannad, . Sabir, A. Mohammed et al., Human emotion classification based on respiration signal, Proceedings of the International Conference on Information and Communication Technology, pp.239-245, 2019.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradientbased learning applied to document recognition, Proc. IEEE, vol.86, pp.2278-2324, 1998.

Y. Luo, . Li-zhen, B. Zhu, and . Lu, A GAN-Based Data Augmentation Method for Multimodal Emotion Recognition, International Symposium on Neural Networks, pp.141-150, 2019.

M. Muszynski, L. Tian, C. Lai, J. Moore, T. Kostoulas et al., Recognizing induced emotions of movie audiences from multimodal information, IEEE Transactions on Affective Computing, 2019.

J. A. , A circumplex model of affect, Journal of Personality and Social Psychology, vol.39, pp.1161-1178, 1980.

P. Sarkar and A. Etemad, Self-supervised Learning for ECG-based Emotion Recognition, 2019.

L. Soëlie, B. Patrice, B. Emmanuel, and M. Elisabeth, Influence des lexiques d'émotions et de sentiments sur l'analyse de sentiments-Application à des critiques de livres, COnférence en Recherche d'Informations et Applications-CORIA 2019, 16th French Information Retrieval Conference. Villeurbanne, 2019.

A. T. Wieckowski and S. W. White, Attention Modification to Attenuate Facial Emotion Recognition Deficits in Children with Autism: A Pilot Study, Journal of Autism and Developmental Disorders, vol.50, pp.30-41, 2020.

A. Yaghoubiy, K. Seyed-kamaledin-setarehdan, and . Maghooli, Emotion Extraction from Video Fragments using Gaze Tracking and AdaBoost Classifier, Majlesi Journal of Electrical Engineering, vol.13, pp.67-81, 2019.