EyeDentify

EyeDentify is a real-time face recognition web application built using Flask, OpenCV, and the face_recognition library. It captures video from a webcam, processes the frames in real time, and identifies faces based on a set of pre-encoded images. The app uses Supabase for both facial data (Postgres) and cloud image storage.

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🚀 Features

• 🔍 Real-time face recognition using webcam
• 🧠 Utilizes pre-trained face encodings from local image files
• 🖥️ Web-based UI with live video and recognition feedback
• ☁️ Supabase integration for facial database and cloud image storage
• 🧰 Modular and easy-to-extend code structure

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🛠️ Tech Stack

• Frontend: HTML5, JavaScript (WebRTC, Canvas API)
• Backend: Python (Flask)
• Libraries: OpenCV, face_recognition, NumPy, Pillow
• Database & Storage: Supabase (Postgres, Storage)

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🖼️ Preparing Your Dataset

1. Place clear face images in the Images/ folder.
2. Each image file name (without extension) will be used as the label.
3. Example:

   Images/
   ├── Alice.jpg
   ├── Bob.png
   

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☁️ Supabase Integration

1. Supabase Client Setup

• The project uses two Supabase clients:
  • supabase1: For database (Postgres) operations (employee info, attendance).
  • supabase2: For storage operations (image upload/list).
• API keys are loaded from .env:
  • spbkey for database access
  • sb_sr_key for storage access

See supabase_client.py:

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⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/eyedentify.git
cd eyedentify

2. Install Dependencies

Ensure you're using Python 3.7 or higher.

pip install -r requirements.txt

Note: face_recognition depends on dlib. For installation help on different platforms, refer to the face_recognition GitHub.

3. Configure Environment Variables

Create a .env file in your project root with your Supabase keys:

spbkey=your_supabase_anon_key
sb_sr_key=your_supabase_service_role_key
sburl=your_supabase_project_url

4. Run the Application

python app.py

Then open your browser and go to: http://localhost:5000

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🧪 How It Works

• On launch, the app encodes all face images from Images/ and uploads new ones to Supabase Storage.
• The browser captures webcam frames and sends them to the backend.
• The backend compares the incoming face encodings with the known ones.
• The details and ID of the matched face are fetched from the Supabase database.
• The last login time is updated in Supabase.
• Results are displayed in the UI.

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📁 Project Structure

EyeDentify/
├── app.py
├── encode_imgs.py
├── supabase_client.py
├── Images/
├── EncodeFile.p
├── requirements.txt
├── .env
├── static/
│   ├── css/
│   │   └── styles.css
│   └── js/
│       └── script.js
└── templates/
    └── index.html

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📜 License

This project is licensed under the GNU General Public License v3.0. You may redistribute, modify, and share the software under the terms of the GPL.

For more details, see the full license text here: GNU GPL v3.0

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