Facial Expression Recognition (FER) for Mental Health Detection Using Transformer Model

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.

📘 Overview of Facial Expression Recognition Techniques Using Python

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.

This repository combines Artificial Intelligence for Mental Health with advanced Facial Emotion Recognition techniques to identify subtle changes in expressions that indicate mental health risks. The project leverages cutting-edge models, including Swin Transformers, Vision Transformers (ViT), and Custom CNNs, integrated with robust datasets such as FER2013 and CK+. These models are designed to:

• 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.

Key Features:

• 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.

📂 AI for Mental Health Repository Structure

📦FER-for-Mental-Health-Detection
├── 📁 Models
│   ├── 📁 Swin_Transformer
│   ├── 📁 Custom_CNN
│   ├── 📁 ViT_Model
│   └── 📁 Other_Models
├── 📁 datasets
├── 📁 images
├── 📁 utilities
├── 📄 README.md
├── 📄 usage_guide.md
├── 📄 LICENSE
└── 📄 requirements.txt

• Models: Contains different subfolders for Swin Transformer, Custom CNN, ViT, and other models.
• datasets: Includes FER2013, CK+, and Genius HR datasets.
• images: Visualizations such as Grad-CAM, architecture diagrams, and augmented samples.
• utilities: Scripts for data preprocessing, augmentation, and evaluation.
• usage_guide.md: Detailed guide to using the models.

📚 Datasets

FER2013

• Description: A dataset of 35,887 grayscale images labeled with seven emotions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral).
• Source: FER2013 on Kaggle.

CK+

• Description: A smaller dataset of 920 images with eight emotion labels.
• Source: CK+ Dataset Official Site.

Genius HR Dataset

• Description: A real-world dataset for workplace mental health analysis.
• Source: Proprietary dataset. Contact for access.

🚀 Emotion Detection Using Python Installation

Prerequisites

• Python 3.10+
• PyTorch 2.0+
• CUDA-enabled GPU (recommended)

Installation Steps

Follow these detailed steps to set up the project and avoid any errors during installation.

1. Clone the Repository

Start by cloning the repository to your local machine and navigate into the project directory:

# Clone the repository
git clone https://github.com/mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-.git

# Navigate into the project directory
cd Facial-Expression-Recognition-FER-for-Mental-Health-Detection-

2. Create a Virtual Environment

It is highly recommended to use a virtual environment to manage dependencies. Run the following commands based on your operating system:

# For Linux/MacOS
python3 -m venv venv
source venv/bin/activate

# For Windows
python -m venv venv
.\venv\Scripts\activate

3. Update pip and Install Dependencies

Ensure your pip is up-to-date and install all required dependencies listed in requirements.txt:

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

4. Verify Installation

To confirm that everything is installed correctly, run the following commands:

# 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

5. Download the FER2013 Dataset

This project uses the FER2013 dataset. Follow these steps to download and prepare the dataset:

# Install Kaggle CLI
pip install kaggle

# Move your Kaggle API token to the appropriate location
mkdir ~/.kaggle
mv /path/to/kaggle.json ~/.kaggle/
chmod 600 ~/.kaggle/kaggle.json

# Download the FER2013 dataset
kaggle competitions download -c challenges-in-representation-learning-facial-expression-recognition-challenge

# Extract the dataset into the datasets/ folder
mkdir datasets
unzip challenges-in-representation-learning-facial-expression-recognition-challenge.zip -d datasets

6. Preprocess the Dataset

Organize the dataset into train, validation, and test directories:

python utilities/preprocess_data.py

7. Run the Model

To ensure everything is working, run the default Swin Transformer model training script:

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

This command trains the Swin Transformer model on the FER2013 dataset with default settings.

Troubleshooting

If you encounter issues during installation:

• CUDA errors: Ensure you have the correct version of CUDA installed.
• Dependency conflicts: Manually resolve versions in requirements.txt.
• Dataset errors: Verify that the FER2013 dataset is correctly downloaded and extracted.

Installation Complete!

You are now ready to explore Facial Expression Recognition for Mental Health Detection. For more information, refer to the Usage Guide.

💡 Models and Architectures

1. Swin Transformer

• Description: A hierarchical transformer optimized for visual tasks, ideal for facial expression recognition and mental health detection.
• Reference: Swin Transformer Paper

2. Custom CNN

• Description: Lightweight CNN for real-time emotion detection, suitable for AI Emotion Recognition tasks.

3. Vision Transformer (ViT)

• Description: Captures long-range dependencies in facial features for robust facial emotion recognition.
• Reference: ViT Paper

4. Additional Models

• Includes MobileNet, EfficientNet, and hybrid architectures for real-time emotion detection.

📷 Visualizations

Augmented Images

• Visualizes data augmentation techniques used to enhance model robustness.

Model Architecture

• Diagram of the Swin Transformer model optimized for facial expression recognition.

Grad-CAM Visualizations

• Highlights the facial regions influencing the model’s predictions.

Mental Health Scoring Summary

📈 Applications

• Human Resources: Monitor and assess employee mental health using AI for mental health detection.
• Healthcare: Real-time emotion detection for early mental health interventions.
• Research: Advance the field of artificial intelligence in mental health detection.

📄 Citation

This research has been published in Engineering, Technology & Applied Science Research, indexed in Scopus Q2. Below is the certification evidence:

Citation Formats

APA:

Mujiyanto, M., Setyanto, A., Kusrini, K., & Utami, E. (2024). Swin Transformer with Enhanced Dropout and Layer-wise Unfreezing for Facial Expression Recognition in Mental Health Detection. Engineering, Technology & Applied Science Research, 14(6), 19016–19023. https://doi.org/10.48084/etasr.9139

MLA:

Mujiyanto, M., et al. "Facial Expression Recognition (FER) for Mental Health Detection." Engineering, Technology & Applied Science Research, vol. 14, no. 6, 2024, pp. 19016-19023.

Vancouver:

Mujiyanto M, et al. Facial Expression Recognition (FER) for Mental Health Detection. Engineering, Technology & Applied Science Research. 2024;14(6):19016-23.

📧 Contact

For questions or support, please contact:

• Email: mujiyanto@amikom.ac.id

Special Credit

• Description: Indonesia artificial intelligence AI Developer | Website Developer | Mobile Developer | Software Developer | Software House Indonesia
• Reference: Second Vision Corp