Veriguard

A face recognition-based attendance and access control system built as a final year project.
Veriguard provides secure, automated attendance marking with anti-spoofing measures and a web-based management dashboard.

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Demo: 'https://veriguard101.netlify.app/'

Facial Recognition with JavaScript using face-api.js

To start up the app:

1. run npm install in the root directory
2. run node on server.js
3. go to http://localhost:5000

Loading 4 primary models

    await Promise.all([
        faceapi.nets.ssdMobilenetv1.loadFromUri('./models'),
        faceapi.nets.faceLandmark68Net.loadFromUri('./models'),
        faceapi.nets.faceRecognitionNet.loadFromUri('./models'),
        faceapi.nets.ageGenderNet.loadFromUri('./models'),
    ])

🚀 Features

• Real-time face recognition in the browser using face-api.js (TensorFlow.js).
• Automated attendance marking with high accuracy (>90% in tests).
• Anti-spoofing detection (detects attempts with photos, screens, or videos).
• Unknown person detection with alerts.
• Management Dashboard:
  • Monitor live camera feed
  • View attendance logs
  • Manage enrolled users
  • Export attendance records
• Secure Backend:
  • Node.js + Express.js for APIs
  • MongoDB for storing users, attendance, and logs
  • JWT authentication for administrators

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🛠️ Tech Stack

• Frontend: HTML5, CSS3, JavaScript, face-api.js,
• Backend: Node.js, Express.js
• Database: MongoDB
• Architecture: Client-side recognition with RESTful APIs

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⚙️ Installation & Setup

1. Clone the repository

   git clone https://github.com/ahmadali47/Veriguard

2. Backend Setup

npm install npm start

3. Frontend Setup

Open frontend/index.html in your browser.

Allow camera permissions when prompted.

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📊 Performance

Accuracy: 90% in ideal conditions

Processing speed: ~280ms per frame

Spoof detection success rate: 80%

User feedback:

95% found UI intuitive

90% reported it was faster than manual systems

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🔒 Limitations

Performance depends on client hardware and lighting conditions.

Enrollment requires cooperation (clear face capture).

Currently no mobile app version.

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🔮 Future Improvements

Mobile application development.

Advanced analytics & predictive reporting.

ERP system integration.

Better performance in low-light environments.

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👨‍💻 Authors

Ahmad Ali

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📜 License

This project is developed for academic purposes. You may use and modify it under an open-source license (MIT recommended).

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