Keywords
How to Cite
Abstract
This scientific article examines the capabilities of Artificial Intelligence (AI) systems in detecting and analyzing human emotions, namely modern technological advancements in the field of "Affective Computing." The article analyzes algorithms for classifying emotions through facial expressions, speech intonation, text content, and physiological indicators, with particular attention to the role of Deep Learning and Convolutional Neural Networks (CNN). The research thoroughly explores the practical significance of emotion recognition technologies in economics, medicine, education, and security, as well as the ethical issues and data privacy concerns that arise in this process. The article also compares the effectiveness of multimodal emotion recognition systems against traditional methods and provides scientific conclusions on future development trends. This work serves as a theoretical and practical resource for AI specialists and information technology students.
References
1. Picard, R. W. (1997). Affective Computing. MIT Press.
2. Ekman, P. (2003). Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. Times Books.
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
4. Pantic, M., & Rothkrantz, L. J. (2000). Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence.
5. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
6. Sariyanidi, E., Gunes, H., & Cavallaro, A. (2015). Automatic analysis of facial affect: A survey of registration, representation, and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence.
7. Schuller, B. W. (2018). Speech Emotion Recognition: Two Decades in a Nutshell, Benchmarks, and Ongoing Trends. Communications of the ACM.
8. Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint.
9. Zadeh, A., et al. (2017). Tensor Fusion Network for Multimodal Sentiment Analysis. EMNLP.
10. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology.
11. Resolution PQ-4996 of the President of the Republic of Uzbekistan dated February 17, 2021 "On measures to create conditions for accelerating the introduction of artificial intelligence technologies".
12. Vinciarelli, A., et al. (2009). Social signal processing: Survey of an emerging domain. Image and Vision Computing.
13. Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2017). AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild. IEEE Transactions on Affective Computing.
14. Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems.
15. Tao, J., & Tan, T. (2005). Affective computing: A review. International Conference on Affective Computing and Intelligent Interaction.
16. Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing.
17. Calvo, R. A., & D'Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing.
18. Grave, E., et al. (2018). Learning Word Vectors for 157 Languages. Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018).
This work is licensed under a Creative Commons Attribution 4.0 International License.
