Down Syndrome Detection Using Facial Analysis

🧬 Project Overview

This computer vision project aims to detect potential Down syndrome markers in children between 0-6 years old through sophisticated facial feature analysis. By combining cutting-edge machine learning techniques with facial landmark detection, the system provides an innovative approach to screening. The developed model is then used in the DownCare app to assist in early detection and support.

✨ Key Features

• 🔍 Precise Facial Landmark Detection
• 📊 Feature Extraction
  • Local Binary Pattern (LBP) features
  • Geometric facial measurements
• 🤖 Machine Learning Classification

🛠 Technical Stack

Dependencies

• OpenCV
• NumPy
• Dlib
• scikit-image
• scikit-learn
• Matplotlib
• joblib

System Requirements

• Python 3.7+
• 64-bit Operating System
• Minimum 8GB RAM
• GPU Recommended (Optional)

🔬 Methodology

Feature Extraction Techniques

1. Facial Landmark Detection
   • 68-point facial landmark identification
   • Advanced symmetry analysis
2. Local Binary Pattern (LBP)
   • Texture feature extraction
   • Captures micro-level facial variations
3. Geometric Feature Analysis
   • Precise inter-landmark distance measurements
   • Structural facial characteristic evaluation

Machine Learning Approach

• Supervised classification model
• Probabilistic prediction

📊 Performance Metrics on Test

Metric	Value
Accuracy	93%
Precision	93%
Recall	93%
AUC	98%

⚠️ Important Disclaimers

🩺 Medical Disclaimer

• This tool is for screening purposes only
• Cannot replace professional medical diagnosis
• Intended as a supportive technological aid

🔮 Future Roadmap

• Expand training dataset
• Implement deep learning architectures
• Real-time detection capabilities