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- Vol. 5 Issue 4
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Stress Detection Using Text Analysis and Facial Recognition
Authors
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Assistant Professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Article Information
- Published In
- Volume 5, Issue 4 (2025)
- Published Online
- July-August 2025
- Pages
- 39-43
- Article Type
- Original Article
References
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2. J. Zhang, X. Chen, and Y. Li, “Real-time mental stress detection using multimodal deep learning,” Frontiers in Neuroscience, vol. 16, pp. 1–11, 2022.
3. M. Hosseini, M. Bodaghi, R. T. Bhupatiraju, A. Maida, and R. Gottumukkala, “Multimodal stress detection using facial landmarks and biometric signals,” arXiv preprint arXiv:2311.03606, Nov. 2023.
4. J. Z. Xiang, L. Liu, and H. Wu, “A multimodal deep learning-based stress detection method using physiological signals,” Frontiers in Physiology, vol. 16, pp. 1–12, 2025.
5. E. Soufleri, P. D. Bamidis, and S. L. Smith, “Enhancing stress detection on social media through multimodal fusion of text and synthesized visuals,” in Proc. BioNLP Workshop, 2025, pp. 24–34.
6. H. P. Chandika, B. Soumya, B. N. E. Reddy, and B. M. S. SaiManideep, “Real-time stress detection and analysis using facial emotion recognition,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, pp. 45–51, Mar. 2024.
7. M.-H. Yi, K.-C. Kwak, and J.-H. Shin, “HyFusER: Hybrid multimodal transformer for emotion recognition using dual cross-modal attention,” Applied Sciences, vol. 15, no. 3, pp. 1053–1068, 2025.
8. I. T. Pavlidis, J. Dowdall, N. Sun, C. Puri, and J. Levine, “Interacting with human physiology through thermal facial imaging,” IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1107–1114, Nov.–Dec. 2010.
9. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015.
10. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, and M. Kudlur, “TensorFlow: A system for large-scale machine learning,” in Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016, pp. 265–283.
2. J. Zhang, X. Chen, and Y. Li, “Real-time mental stress detection using multimodal deep learning,” Frontiers in Neuroscience, vol. 16, pp. 1–11, 2022.
3. M. Hosseini, M. Bodaghi, R. T. Bhupatiraju, A. Maida, and R. Gottumukkala, “Multimodal stress detection using facial landmarks and biometric signals,” arXiv preprint arXiv:2311.03606, Nov. 2023.
4. J. Z. Xiang, L. Liu, and H. Wu, “A multimodal deep learning-based stress detection method using physiological signals,” Frontiers in Physiology, vol. 16, pp. 1–12, 2025.
5. E. Soufleri, P. D. Bamidis, and S. L. Smith, “Enhancing stress detection on social media through multimodal fusion of text and synthesized visuals,” in Proc. BioNLP Workshop, 2025, pp. 24–34.
6. H. P. Chandika, B. Soumya, B. N. E. Reddy, and B. M. S. SaiManideep, “Real-time stress detection and analysis using facial emotion recognition,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, pp. 45–51, Mar. 2024.
7. M.-H. Yi, K.-C. Kwak, and J.-H. Shin, “HyFusER: Hybrid multimodal transformer for emotion recognition using dual cross-modal attention,” Applied Sciences, vol. 15, no. 3, pp. 1053–1068, 2025.
8. I. T. Pavlidis, J. Dowdall, N. Sun, C. Puri, and J. Levine, “Interacting with human physiology through thermal facial imaging,” IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1107–1114, Nov.–Dec. 2010.
9. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015.
10. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, and M. Kudlur, “TensorFlow: A system for large-scale machine learning,” in Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016, pp. 265–283.
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