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Библиографический список

1. Hossain M.A., Assiri B. Facial expression recognition based on active region of interest using deep learning and parallelism // PeerJ Comput Sci. 2022. Vol. 8. P. e894.

2. Ren S. et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017. Vol. 39, № 6. P. 1137–1149.

3. Li Y. et al. Online Human Action Detection Using Joint Classification-Regression Recurrent Neural Networks // Computer Vision – ECCV 2016 / ed. Leibe B. et al. Cham: Springer International Publishing, 2016. P. 203–220.

4. De A., Saha A. A comparative study on different approaches of real time human emotion recognition based on facial expression detection // 2015 International Conference on Advances in Computer Engineering and Applications. 2015. P. 483–487.

5. Canal F.Z. et al. A survey on facial emotion recognition techniques: A state-of-the-art literature review // Information Sciences. 2022. Vol. 582. P. 593–617.

6. Yang, B., Cao J.M., Jiang, D.P. Facial expression recognition based on dual-feature fusion and improved random forest classifier // Multimed Tools Appl. 2018. Vol. 77. P. 20477–20499.

7. Ляшов М.В. et al. Нейросетевая система отслеживания и распознавания объектов в видеопотоке // Современные наукоемкие технологии,. 2018. Vol. № 12-1,. P. 102–107.

8. Hess U., Blairy S. Facial mimicry and emotional contagion to dynamic emotional facial expressions and their influence on decoding accuracy // International Journal of Psychophysiology. 2001. Vol. 40, № 2. P. 129–141.

9. Schneider S. et al. The impact of video lecturers’ nonverbal communication on learning – An experiment on gestures and facial expressions of pedagogical agents // Computers & Education. 2022. Vol. 176. P. 104350.

10. Goodfellow I.J. et al. Challenges in Representation Learning: A report on three machine learning contests: arXiv:1307.0414. arXiv, 2013.

11. Liu X. iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis // iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis. 2021.

12. Ranganathan H., Chakraborty S., Panchanathan S. Multimodal emotion recognition using deep learning architectures // 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). 2016. P. 1–9.

13. Gunes H, Piccardi M. A bimodal face and body gesture database for automatic analysis of human nonverbal affective behavior // 18th International conference on pattern recognition (ICPR’06). 2006. Vol. 1. P. 1148–1153.

14. Обзор современных подходов к определению эмоций человека на основе интеллектуального анализа видео // Университет ИТМО, Санкт-Петербургский Федеральный исследовательский центр Российской академии наук, / ed. Кашевник А.М., Шушкова В.В. 2024.

15. Горский Г.Е. Анализ нейросетевых архитектур для распознавания объектов на видео // Научный аспект, Московский государственный технический университет им. Н. Э. Баумана. 2024. № №6-2024.