IDVision

IDVision is a desktop application for seamless ID verification and face authentication.
It leverages IDAnalyzer for extracting information from both sides of identity documents, then verifies user authenticity with live facial checks.

Features

• Dual-Side ID OCR: Extracts and matches data from front and back of ID cards.
• ID Verification: Checks document authenticity and expiration using IDAnalyzer API.
• Face Verification: Live webcam face matching with ID photo.
• Liveness Detection: Prevents spoofing using blink, expression, and movement checks.
• Modern OOP Python Codebase: Easy to extend, integrate, or maintain.

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Tech Stack

• Python 3.7+
• PyQt5: GUI framework
• OpenCV: Camera and image processing
• IDAnalyzer API: OCR and ID verification
• Torch / Facenet / VGGFace2: Face recognition
• Custom liveness modules (blink, face orientation, emotion prediction)

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Setup

1. Clone the Repo

git clone https://github.com/co-dev0909/idvision.git
cd idvision

2. Install Dependencies

You can use pip (preferably in a virtual environment):

pip install -r requirements.txt

Main dependencies (see requirements.txt):

• PyQt5
• opencv-python
• torch
• facenet-pytorch (or your face models)
• requests
• python-dotenv

3. Configure API Key

Obtain your API key from IDAnalyzer.

Create a .env file in your project root:

IDANALYZER_API_KEY=your_api_key_here
IDANALYZER_REGION=US

4. Run the App

python main.py

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Usage

1. Select ID Images: Upload both the front and back images of an ID card.
2. Extract & Verify: Click the "Verify" button to extract and display information and status.
3. Face Verification: Proceed to face authentication and liveness checks using your webcam.
4. Result: The app displays the match status and verification result.

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Project Structure

idvision/
├── gui/
│   ├── page1_idcard.py   # ID card selection & OCR page
│   ├── page2.py          # Face verification page
│   ├── page3.py          # Liveness challenge page
│   └── utils.py
├── idanalyzer_api.py      # IDAnalyzer API integration class
├── liveness_detection/
│   └── ...               # Blink, emotion, orientation detection
├── verification_models.py # Face verification models
├── main.py               # Main app entry point
├── requirements.txt
└── .env                  # Environment config (not in git)

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Customization

• Switching API Providers: Update idanalyzer_api.py for a different OCR or KYC provider.
• Adding Fields: Extend _extract_fields in idanalyzer_api.py to extract more data.
• Changing GUI: All screens are in gui/ as PyQt widgets—extend or restyle as needed.

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License

MIT This project is provided as-is for demo and integration purposes. Always comply with privacy and legal guidelines in your jurisdiction when handling user ID data.

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Credits

• IDAnalyzer for the KYC API
• PyQt5 for GUI
• OpenCV and Torch for face/liveness processing

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