Facial Expression Recognition FER For Mental Health Detection

Abstract

1. Introduction

Mental health issues such as anxiety, depression, OCD (Obsessive Compulsive Disorder), PTSD (Post-Traumatic Stress Disorder), and other conditions significantly impact individuals and society. Early detection and intervention can drastically improve outcomes, and Facial Expression Recognition (FER) provides a non-invasive and efficient way to monitor emotional states.

Welcome to the Facial Expression Recognition (FER) for Mental Health Detection repository. This project leverages cutting-edge AI models, including Swin Transformer, to analyze facial expressions for detecting mental health conditions. For detailed insights, refer to the research paper published in Engineering, Technology & Applied Science Research, indexed in Scopus Q2.

2. Objective

Facial Expression Recognition (FER) for Mental Health Detection applies AI models like Swin Transformer, CNN, and ViT for detecting emotions linked to anxiety, depression, PTSD, and OCD. It focuses on AI for mental health, emotion detection using OpenCV Python, and real-time applications in healthcare and HR systems.

This project demonstrates how Python, Computer Vision, Deep Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Recognize emotions like happiness, sadness, anger, fear, and surprise.
• Detect early signs of mental health conditions such as serious mental illness and stress-related disorders.
• Provide practical applications in AI Emotion Recognition for healthcare, HR, and research.
• High-accuracy emotion detection using deep learning for facial expression recognition.
• Integration with mental health scoring systems to quantify emotional health.
• Applications in real-time emotion detection systems and emotion detection using OpenCV Python.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-.git
cd Facial-Expression-Recognition-FER-for-Mental-Health-Detection-

pip install --upgrade pip
pip install -r requirements.txt

# Check Python version
python --version
# Output should be Python 3.10+

# Check PyTorch installation
python -c "import torch; print(torch.__version__)"
# Output should match the PyTorch version specified in requirements.txt

python utilities/train_model.py --model swin_transformer --epochs 10 --batch_size 32

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by mujiyantosvc and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.