Turanio AWS Rekognition

Face recognition and comparison using AWS Rekognition API

🎯 Features

• Face Detection: Detect faces in images with detailed analysis
• Face Comparison: Compare two faces and get similarity scores
• AWS Integration: Powered by AWS Rekognition API
• Real-time Analysis: Fast face recognition results
• HTTPS Support: Secure communication with SSL/TLS
• Image Upload: Direct image upload and analysis

📸 Demo

🚀 Quick Start

Prerequisites

• Node.js 14+ installed
• AWS Account with Rekognition enabled
• AWS credentials (Access Key ID and Secret Access Key)

Installation

# Clone the repository
git clone https://github.com/turanalmammadov/turanio-aws-rekognition.git

# Navigate to project directory
cd turanio-aws-rekognition

# Install dependencies
npm install

Configuration

1. AWS Credentials Setup

Create assets/aws-config.json with your AWS credentials:

{
  "accessKeyId": "YOUR_AWS_ACCESS_KEY_ID",
  "secretAccessKey": "YOUR_AWS_SECRET_ACCESS_KEY",
  "region": "us-east-2"
}

⚠️ Security Warning: Never commit real AWS credentials to version control!

2. SSL Certificates (Optional)

For HTTPS support, place your SSL certificates in assets/:

• assets/key.pem - Private key
• assets/certificate.pem - SSL certificate

For development, you can generate self-signed certificates:

openssl req -x509 -newkey rsa:4096 -keyout assets/key.pem -out assets/certificate.pem -days 365 -nodes

Running the Application

# Start the server
npm start

# Server will run on:
# HTTP:  http://localhost:80
# HTTPS: https://localhost:5555

📡 API Endpoints

POST /doFaceAnalysis

Analyze a single face in an image.

Request:

{
  "image": "data:image/png;base64,iVBORw0KGgoAAAANS..."
}

Response:

{
  "FaceDetails": [{
    "BoundingBox": { ... },
    "Confidence": 99.9,
    "Emotions": [...],
    "AgeRange": { ... },
    "Gender": { ... }
  }]
}

POST /doCompare

Compare two faces and get similarity score.

Request:

{
  "leftImage": "data:image/png;base64,...",
  "rightImage": "data:image/png;base64,..."
}

Response:

{
  "FaceMatches": [{
    "Similarity": 95.5,
    "Face": { ... }
  }],
  "UnmatchedFaces": []
}

POST /savePics

Save uploaded images to server (for testing).

Request:

{
  "leftImage": "data:image/png;base64,...",
  "rightImage": "data:image/png;base64,..."
}

🏗️ Architecture

┌─────────────┐      HTTPS      ┌──────────────┐      AWS SDK      ┌─────────────────┐
│   Client    │ ───────────────> │ Express API  │ ────────────────> │ AWS Rekognition │
│  (Browser)  │                  │   (Node.js)  │                   │      API        │
└─────────────┘ <─────────────── └──────────────┘ <──────────────── └─────────────────┘
                  JSON Response        Process                Result

🛠️ Technologies

• Backend: Node.js, Express.js
• Cloud: AWS Rekognition
• Security: HTTPS with SSL/TLS
• Image Processing: Base64 encoding/decoding
• CORS: Cross-origin resource sharing enabled

🔒 Security Considerations

• ⚠️ Never commit AWS credentials to version control
• ✅ Use environment variables or AWS IAM roles in production
• ✅ Implement rate limiting for production use
• ✅ Add authentication/authorization middleware
• ✅ Validate and sanitize all inputs
• ✅ Use HTTPS in production

📝 Development

Project Structure

turanio-aws-rekognition/
├── assets/
│   ├── aws-config.json      # AWS credentials (gitignored)
│   ├── key.pem              # SSL private key
│   └── certificate.pem      # SSL certificate
├── index.html               # Frontend interface
├── server.js                # Express server + AWS integration
├── package.json             # Dependencies
└── README.md                # This file

Environment Variables

You can use environment variables instead of config file:

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION="us-east-2"
export PORT=3000

🧪 Testing

# Start the server
npm start

# Open browser
open http://localhost:80

# Or test API directly
curl -X POST http://localhost:80/doFaceAnalysis \
  -H "Content-Type: application/json" \
  -d '{"image":"data:image/png;base64,..."}'

📚 AWS Rekognition Features Used

• DetectFaces: Detect faces and analyze attributes

  • Age range estimation
  • Gender detection
  • Emotion analysis
  • Face landmarks
  • Quality assessment

• CompareFaces: Compare facial features between images

  • Similarity scoring (0-100%)
  • Bounding box coordinates
  • Confidence levels

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Links

• AWS Rekognition Documentation
• AWS SDK for JavaScript
• Project Repository

⚡ Performance

• Face detection: ~1-2 seconds per image
• Face comparison: ~1-2 seconds per pair
• Depends on image size and AWS region latency

🆘 Troubleshooting

Common Issues

Error: "Missing credentials"

• Ensure assets/aws-config.json exists and has valid credentials
• Or set AWS environment variables

Error: "AccessDenied"

• Check AWS IAM permissions for Rekognition
• Required permissions: rekognition:DetectFaces, rekognition:CompareFaces

Error: "Region not configured"

• Verify region in aws-config.json
• Ensure region supports Rekognition

HTTPS not working

• Generate SSL certificates (see Configuration section)
• Check if port 5555 is available

🌟 Author

Turan Almammadov

• GitHub: @turanalmammadov
• Website: turanalmammadov.com

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