Scan faces to instantly access profiles with our powerful Django-based facial recognition system
graph LR
A[Capture Image] --> B[Face Detection]
B --> C[Face Encoding]
C --> D[Database Matching]
D --> E{Match Found?}
E -->|Yes| F[Redirect to Profile]
E -->|No| G[Profile Not Found]
style A fill:#ff9900,stroke:#333,stroke-width:2px
style E fill:#36B37E,stroke:#333,stroke-width:2px
style F fill:#36B37E,stroke:#333,stroke-width:2px
style G fill:#FF5630,stroke:#333,stroke-width:2px
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pie title InstaProfile Technology Stack
"Django" : 35
"OpenCV" : 30
"Face Recognition" : 25
"Frontend (HTML/CSS/JS)" : 10
# Clone repository
git clone https://github.com/yourusername/instaprofile.git
cd instaprofile
# Setup environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
# Install additional dependencies
pip install opencv-python face-recognition dlib
# Initialize application
python manage.py migrate
python manage.py createsuperuser
python manage.py runserver
# Access at http://127.0.0.1:8000/- Python 3.8+
- Django 4.0+
- OpenCV 4.5+
- face_recognition library
- dlib
- Webcam or camera access for face scanning
flowchart TD
A[User] -->|Uploads Image| B[Django Frontend]
B -->|Process Image| C[OpenCV Module]
C -->|Extract Features| D[Face Recognition]
D -->|Face Encodings| E[Django Backend]
E -->|Query| F[(Database)]
F -->|Return Match| E
E -->|Profile Data| B
B -->|Display Profile| A
style A fill:#ff9900,stroke:#333,stroke-width:2px
style F fill:#36B37E,stroke:#333,stroke-width:2px
| Step | Description | Technology |
|---|---|---|
| 1. Face Detection | Locate and isolate faces in the image | OpenCV / HOG detector |
| 2. Face Alignment | Normalize face position and orientation | dlib |
| 3. Feature Extraction | Extract 128-dimensional face encoding | face_recognition |
| 4. Database Matching | Compare encoding with stored profiles | Django ORM / NumPy |
| 5. Profile Retrieval | Fetch matching profile information | Django |
InstaProfile takes privacy seriously. Our implementation includes:
- Opt-in only: Users must explicitly consent to facial recognition
- Data encryption: All facial encodings are encrypted at rest
- Limited data storage: Only facial encodings are stored, not raw images
- Deletion options: Users can delete their facial data at any time
- Access controls: Strict permission system for profile access
graph TD
A[InstaProfile] --> B[Networking Events]
A --> C[Corporate Directories]
A --> D[Campus Applications]
A --> E[Social Media Integration]
A --> F[Security Systems]
style A fill:#ff9900,stroke:#333,stroke-width:2px
Contributions are welcome! We're particularly interested in:
- Improving recognition accuracy
- Adding support for more platforms
- Enhancing security features
- Optimizing performance
Click to expand contribution guidelines
- Fork the project
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Please ensure your code follows our coding standards and includes appropriate tests.
Distributed under the MIT License. See LICENSE for more information.
Built with computer vision and web technology by Bhuvan Vendotha
⭐ Star this repo if you find it useful! ⭐