The week's pick
Reseach Article
Real-Time Depression Detection using Emotion Recognition Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 66 |
| Year of Publication: 2025 |
| Authors: Sonali Singh, Navita Srivastava |
10.5120/ijca2025924467
|
Sonali Singh, Navita Srivastava . Real-Time Depression Detection using Emotion Recognition Techniques. International Journal of Computer Applications. 186, 66 ( Feb 2025), 40-48. DOI=10.5120/ijca2025924467
Abstract
When it comes to health, mental health is a very widespread problem today. health, where nearly 25% of the world's population suffers from some mental illness. Artificial Intelligence (AI) is a concept that has evolved considerably and is expected to soon bring improvements and help to people in various fields. The mental health field is not excluded, so artificial intelligence can help in the performance of health services, whether by medical staff or patients. Within mental health, it has been observed that by recognizing facial expressions, depression, schizophrenia, or other similar conditions. However, for machine learning, a large data set is required for good accuracy. In this paper, presented a facial expression recognition method using only a few training datasets, the accuracy of this training dataset was 94%. This precision is stored as the depression-detector model name.h5. The proposed model (FEDA) will facilitate the understanding of people's mental state. Human image data models have helped understand emotions and provided new application concepts in health, security, business, and education, which can be used remotely via a web application.
References
- Astha Sharma (M. Tech Scholar) and Surender Kumar Grewal, August 2019, Implementation of human emotion recognition system, SSN NO: 1301-2746, Volume 8, Issue 8.
- P. J. Bota et al. September 2019, Review, Current Challenges, and Future Possibilities on Emotion Recognition Using ML and Physiological Signals, IEEE.
- Sonali Singh and Prof. Navita Srivastava, June 2021, Emotion Recognition using AI Techniques: A Review, Volume 8, Issue 6, ISSN-2349-5162, JETIR. https://www.jetir.org/papers/JETIR2106764.pdf
- Paul Ekman, 1992, An argument for Basic Emotions, cognition, and Emotion, 169-200.
- Neha et al., Aug 2020, Emotion Recognition and Depression Detection using Deep Learning, Volume 7, Issue 8, ISSN-2395-0056, IRJET.
- Vedhas Vinjamuri et al., April 2018, Emotion Recognition Using Real-Time Face Recognition, Volume 6, Issue 2, ISSN- 2320-2882, IJCRT.
- Barathi Subramanian et al., July 2022, Digital Twin Model: A Real-Time Emotion Recognition System for Personalized Healthcare, Volume 10, IEEE.
- Emotion recognition: introduction to emotion reading technology, Dubai Internet City Building 3, https://recfaces.com/articles/emotion-recognition
- Khamar Basha Shaik et al., 2015, Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space, International Conference on Recent Trends in Computing, http://creativecommons.org/licenses/by-nc-nd/4.0/
- S. Kolkur et al., 2017, Human Skin Detection Using RGB, HSV and YCbCr Color Models, Vol. 137, Pp. 324-332, The authors. Published by Atlantis Press, http://creativecommons.org/licens)es/by-nc/4.0/
- Jin Hyun Cheong et al., August 2023, Py‑Feat: Python Facial Expression Analysis Toolbox, Springer, doi.org/10.1007/s42761-023-00191-4.
- Hamid Golzadeh et al., 2018, Emotion Recognition using spatiotemporal features from Facial Expression Landmarks, IEEE.
- Fuzail Khan, Jul 2020, Facial Expression Recognition using Facial Landmark Detection and Feature Extraction via Neural Networks, arXiv:1812.04510v3 [cs.CV].
- Moon Hwan Kim et al., 2015, Emotion Detection Algorithm Using Frontal Face Image, KINTEX, Gyeonggi-Do, Korea, ICCA.
- Tanusree Podder et al., August 2022, Time efficient real-time facial expression recognition with CNN and transfer learning, Indian Academy of Sciences, https://doi.org/10.1007/s12046-022-01943-x.
- Zeeshan Farhan Hanif et al., May 2023, Emotional Analysis using OpenCV, Volume 8, Issue 5, SSN-2456-4184, IJNRD.
- Jianqiang Xu et al., January 2020, Intelligent Emotion Detection Method Based on Deep Learning in Medical and Health Data, Volume 8, IEEE.
- Mr. Rohan Appasaheb Borgalli and Dr. Sunil Surve, April 2022, Deep Learning Framework for Facial Emotion Recognition using CNN Architectures, IEEE, doi: 10.1109/ICEARS53579.2022.9751735.
- Sunil MP and Hariprasad. S.A, June 2023, Facial Emotion Recognition using a Modified Deep Convolutional Neural Network Based on the Concatenation of XCEPTION and RESNET50 V2, IJEEE, Vol.10, doi.org/10.14445/23488379/IJEEE-V10I6P110.
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