Stress Alarm Raiser Based on Facial Expressions

Shweta Sinha; Aakarsh Sharma

doi:10.55524/ijircst.2023.11.3.9

Authors

Shweta Sinha Associate Professor, Department of Computer Science and Engineering, Amity University, Gurugram, Haryana, India Author
Aakarsh Sharma Research Scholar, Department of Computer Science and Engineering, Amity University, Gurugram, Haryana, India Author

DOI:

https://doi.org/10.55524/ijircst.2023.11.3.9

Keywords:

Emotion Detection, Deep Face, Facial Expression Detection

Abstract

This paper presents the development of a stress detector using facial expression analysis in Python, utilizing the Deep Face library. Also, after detecting whether the person is in stress or not, it allows the user to inform about his stress to the preferred his/her family member by sending an automated WhatsApp message and showing some remedies to reduce stress.

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References

https://www.rti.org/insights/could-multimodal-ai-detect human-emotion

Soleymani, M., Garcia, D., Jou, B., & Schuller, B. (2017). A Survey of Automatic Stress Recognition Approaches in the Wild. ACM Computing Surveys (CSUR), 50(6), 1-36.

https://news.mit.edu/2022/optimized-solution-face recognition-0406

Taheri, M., & Ward, R. (2020). Deep Face: A Deep Learning Facial Recognition Framework for Python. Journal of Open Source Software, 5(53), 2524.

https://news.mit.edu/2022/optimized-solution-face recognition-0406

Khan, Z. A., Naseem, I., & Khan, A. I. (2017). A Review of Automatic Stress Recognition Approaches for Human Computer Interaction. Journal of Ambient Intelligence and Humanized Computing, 8(2), 173-190.

Kaur, G., & Singla, M. (2021). Emotion Recognition using Deep Learning Approaches: A Survey. Artificial Intelligence Review, 54(7), 5401-5434.

Jung, H., & Lee, S. (2020). A Deep Learning Approach for Real-Time Stress Detection using Physiological Signals and Facial Images. International Journal of Environmental Research and Public Health, 17(19), 7133.

Dhall, A., Goecke, R., Joshi, J., Sikka, K., & Gedeon, T. (2013). Emotion Recognition in the Wild Challenge 2013 (EmotiW 2013). Proceedings of the 15th ACM on International Conference on Multimodal Interaction, 509- 516.

Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2016). Affect Net: A Database for Facial Expression, Valence, and Arousal Computing in the Wild. IEEE Transactions on Affective Computing, 10(1), 18-31