Emotion Recognition and Affective Computing
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P121Keywords:
Emotion Recognition, Affective Computing, Human-Computer Interaction, Multimodal AI, Facial Expression Analysis, Speech Emotion Recognition, Physiological Signals, Sentiment Analysis, Empathetic AI, Emotional Intelligence, Human-Centered AIAbstract
Emotion recognition and affective computing represent a rapidly growing interdisciplinary field at the intersection of artificial intelligence, psychology, neuroscience, and human-computer interaction. The goal of affective computing is to develop systems that can perceive, interpret, and respond to human emotions, thereby enabling more natural, adaptive, and empathetic interactions between humans and machines. Modern approaches leverage machine learning, deep learning, and multimodal data including facial expressions, speech signals, physiological responses, and textual content—to detect and classify emotional states with increasing accuracy. Applications of emotion recognition span diverse domains such as healthcare, education, entertainment, customer service, robotics, and mental health monitoring. Despite its potential, the field faces technical, ethical, and societal challenges, including data privacy, cross-cultural variability in emotional expression, interpretability of models, and potential misuse of affective data. This article provides a comprehensive exploration of emotion recognition and affective computing, discussing foundational theories of emotion, sensor technologies, machine learning techniques, multimodal integration, real-world applications, challenges, ethical considerations, and future research directions. By enabling machines to understand and respond to human emotions, affective computing has the potential to revolutionize human-computer interaction, improve wellbeing, and foster more socially intelligent AI systems.
Downloads
References
[1] Chen, Z., Zhang, X., Sun, C., & Gao, C. (2023). Self‑supervised learning for graph neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 34(7), 3300–3323. https://doi.org/10.1109/TNNLS.2022.3224711
[2] Du, Z., Zhu, J., Wu, D., & Wang, J. (2023). Hybrid contrastive self‑supervised learning for medical image segmentation. Computer Methods and Programs in Biomedicine, 229, 107345. https://doi.org/10.1016/j.cmpb.2023.107345
[3] Girdhar, R., Xia, F., Doersch, C., & Zhai, A. (2023). Align and prompt: Self‑supervised learning for cross‑modal video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 4512–4522). IEEE.
[4] Jou, B., Li, W., & Ren, X. (2023). Self‑supervised contrastive learning for robust audio representations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 264–277. https://doi.org/10.1109/TASLP.2023.3245319
[5] Zhang, H., Zhang, Y., & Xu, Q. (2023). Self‑supervised learning with dual attention for scalable natural language understanding. Knowledge‑Based Systems, 264, 110581. https://doi.org/10.1016/j.knosys.2023.110581
[6] Olley, Wilfred Oritsesan, and Francisca Chinazor Alajemba. "Audience’s perception of social media as tools for the creation of fashion awareness." The International Journal of African Language and Media Studies 2, no. 1 (2022): 141.
[7] Wilfred, Olley Oritsesan, EWOMAZINO DANIEL AKPOR, and OBINNA JOHNKENNEDY CHUKWU. "APPLICATION OF AGENDA SETTING, MEDIA DEPENDENCY, AND USES AND GRATIFICATIONS THEORIES IN THE MANAGEMENT OF DISEASE OUTBREAK IN NIGERIA." Euromentor 12, no. 3 (2021).
[8] Ate, Andrew Asan, Ewomazino Daniel Akpor, Wilfred Oritsesan, Sadiq Oshoke Akhor, Edike Kparoboh Frederick, Joseph Omoh Ikerodah, Abdulazeez Hassan Kadiri et al. "Communication and governance for cultural development: Issues and platforms." Corporate & Business Strategy Review 3, no. 2 (2022): 151-158.
[9] Olley, Wilfred Oritsesan, Ewomazino Daniel Akpor, Dike Harcourt-Whyte, Samson Ighiegba Omosotomhe, Afam Patrick Anikwe, Edike Kparoboh Frederick, Evwiekpamare Fidelis Olori, and Paul Edeghoghon Umolu. "Electoral violence and voter apathy: Peace journalism and good governance in perspective." Corporate Governance and Organizational Behavior Review 6, no. 3 (2022): 112-119.
[10] Abdulazeez, Isah, Wilfred O. Olley, and PhD2&Abdulazeez H. Kadiri. "CHAPTER THIRTY ONE SELF-AFFIRMATIVE DISCOURSE ON SOCIAL JUDGEMENT THEORY AND POLITICAL ADVERTISING." Discourses on Communication and Media Studies in Contemporary Society (2022): 258.
[11] Patel, Saumil, Yi Liu, Ruobing Zhao, Xinyu Liu, and Yueqing Li. "Infotainment Display for Different Screen Locations, Menu Types, and Positions." In HCI in Mobility, Transport, and Automotive Systems: 4th International Conference, MobiTAS 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26–July 1, 2022, Proceedings, p. 258. Springer Nature, 2022.
[12] Patel, Saumil. "Human Factor and Ergonomics Evaluation of In-Vehicle Touchscreen Infotainment Display." Master's thesis, Lamar University-Beaumont, 2021.
[13] Patel, Saumil, Yi Liu, Ruobing Zhao, Xinyu Liu, and Yueqing Li. "Inspection of in-vehicle touchscreen infotainment display for different screen locations, menu types, and positions." In International conference on human-computer interaction, pp. 258-279. Cham: Springer International Publishing, 2022.
[14] Jabed, M. M. I., Gupta, A. B., Ferdous, J., Islam, M., & Akter, S. (2022). Self-Supervised Learning for Efficient and Scalable AI: Towards Reducing Data Dependency in Deep Learning Models. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 317–.
[15] Santos, C. (2022). Self-supervised representation learning: Investigating self-supervised learning methods for learning representations from unlabeled data efficiently. Journal of AI-Assisted Scientific Discovery, 2(1).
[16] Routhu, K. K. (2018). Reusable Integration Frameworks in Oracle HCM: Accelerating Enterprise Automation through Standardized Architecture. International Journal of Scientific Research & Engineering Trends, 4(4).
[17] Cao, Y.-H., Sun, P., Huang, Y., Wu, J., & Zhou, S. (2022). Synergistic self-supervised and quantization learning. ArXiv Preprint.
[18] Miller, J. D., Arasu, V. A., Pu, A. X., Margolies, L. R., Sieh, W., & Shen, L. (2022). Self-supervised deep learning to enhance breast cancer detection on screening mammography. ArXiv Preprint.
[19] Routhu, K. K. (2019). Hybrid machine learning architecture for absence forecasting within Oracle Cloud HCM. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1-5.
[20] Routhu, K. K. (2019). Conversational AI in Human Capital Management: Transforming Self-Service Experiences with Oracle Digital Assistant. International Journal of Scientific Research & Engineering Trends, 5(6).
[21] Turrisi da Costa, V. G., Fini, E., Nabi, M., Sebe, N., & Ricci, E. (2022). solo-learn: A Library of Self-supervised Methods for Visual Representation Learning. Journal of Machine Learning Research, 23, 1–6.
[22] Ozsoy, S., Hamdan, S., Arik, S. Ö., & Erdogan, A. T. (2022). Self-supervised learning with an information maximization criterion. In Advances in Neural Information Processing Systems.
[23] Haresamudram, H., Essa, I., & Plötz, T. (2022). Assessing the state of self-supervised human activity recognition using wearables. ArXiv Preprint.
[24] Barbalau, A., Ionescu, R. T., Georgescu, M.-I., et al. (2022). SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection. ArXiv Preprint.
[25] Lemkhenter, A., & Favaro, P. (2022). Towards sleep scoring generalization through self-supervised meta-learning. ArXiv Preprint.
[26] Zhang, C. (2022). A survey on masked autoencoder for self-supervised learning. ArXiv Preprint.
[27] Kranthi Kumar Routhu. (2020). Intelligent Remote Workforce Management: AI, Integration, and Security Strategies Using Oracle HCM Cloud. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1–5. https://doi.org/10.5281/zenodo.17531257
[28] Routhu, K. K. (2020). Strategic Compensation Equity and Rewards Optimization: A Multi-cloud Analytics Blueprint with Oracle Analytics Cloud. Available at SSRN 5737266.
[29] Routhu, K. K. (2019). AI-Enhanced Payroll Optimization: Improving Accuracy and Compliance in Oracle HCM. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1-5.
[30] Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
[31] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021). Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and Regulatory Compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 73-80.
[32] Attipalli, A., Enokkaren, S., BITKURI, V., Kendyala, R., KURMA, J., & Mamidala, J. V. (2021). Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection. Available at SSRN 5741305.
[33] Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 60-69.
[34] Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V. (2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations. International Journal of Emerging Research in Engineering and Technology, 2(2), 64-72.
[35] Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R. R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 83-91.
[36] Attipalli, A., BITKURI, V., KURMA, J., Enokkaren, S., Kendyala, R., & Mamidala, J. V. (2021). A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions. Available at SSRN 5741342.
[37] Routhu, K. K. (2021). AI-augmented benefits administration: A standards-driven automation framework with Oracle HCM Cloud. International Journal of Scientific Research and Engineering Trends, 7(3).
[38] Routhu, K. K. (2021). Harnessing AI Dashboards in Oracle Cloud HCM: Advancing Predictive Workforce Intelligence and Managerial Agility. International Journal of Scientific Research & Engineering Trends, 7(6).
