🎓 Smart Attendance System (Face Recognition-Based)

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📖 Project Overview

The Smart Attendance System is a Face Recognition-Based Attendance Tracker designed for educational institutions and workplaces.
It automates the process of marking attendance by recognizing faces in real-time and securely logging them in a database.

🔑 Key Benefits

• Contactless & hygienic 👋
• Prevents proxy/buddy attendance 🚫
• Real-time data storage & reporting 📊
• Secure, scalable & cloud-ready ☁️

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⚡ System Workflow

flowchart TD
    A[User Face] --> B[Camera Capture]
    B --> C[OpenCV Haarcascade Detection]
    C --> D[Feature Extraction & Encoding]
    D --> E[Compare with Stored Database]
    E -->|Match| F[Attendance Marked ✅]
    E -->|No Match| G[Reject/Registration ❌]
    F --> H[Attendance Stored in DB & CSV]
    H --> I[Reports for Admin/Teacher]

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✨ Features

• 👨‍🏫 Role-Based Access → Admin, Teacher, Student
• 📷 Real-Time Face Detection & Recognition
• 📂 Automated Attendance Logging (DB + CSV)
• 📊 Downloadable Reports (Daily/Weekly/Monthly)
• 📱 Responsive Web UI with Flask + Bootstrap
• 🔒 Secure Authentication with hashed passwords
• ☁️ Cloud-ready Deployment

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🛠️ Tech Stack

Frontend: HTML5, CSS3, JavaScript, Bootstrap
Backend: Python (Flask Framework)
Libraries: OpenCV, NumPy, Pickle, bcrypt
Database: SQLite (default) | MySQL (scalable)
Tools: Git, Heroku/Railway/AWS for deployment

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📂 Project Structure

FaceRecognitionAttendanceSystem/
│
├── app.py                         # Main Flask app
├── schema.sql                     # Database schema
├── attendance.db                  # SQLite database
│
├── /Attendance/                   # Attendance CSV reports
│   └── Attendance_YYYY-MM-DD.csv
│
├── /data/                         # Saved facial data
│   ├── faces_data.pkl
│   ├── names.pkl
│   └── settings.pkl
│
├── /static/                       # Static files (CSS, images)
│   ├── css/style.css
│   └── bg.png
│
├── /templates/                    # HTML Templates
│   ├── index.html
│   ├── login.html
│   ├── dashboard.html
│   ├── capture.html
│   ├── recognize.html
│   ├── attendance.html
│   └── settings.html
│
└── haarcascade_frontalface_default.xml  # Face detection model

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⚙️ Installation & Setup

🔧 Prerequisites

• Python 3.8+
• Webcam/Camera
• SQLite (default)

📥 Step 1: Clone Repository

git clone https://github.com/themanishpndt/Face-Recognition-Attendance-System.git
cd Face-Recognition-Attendance-System

🛠️ Step 2: Create Virtual Environment

python -m venv venv
venv\Scripts�ctivate   # Windows
source venv/bin/activate # Mac/Linux

📦 Step 3: Install Dependencies

pip install -r requirements.txt

🗄️ Step 4: Setup Database

sqlite3 attendance.db < schema.sql

▶️ Step 5: Run Application

python app.py

🌐 Step 6: Access in Browser

http://127.0.0.1:5000/

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📊 Attendance Report Format

Example CSV Report:

Student_ID, Name, Date, Time, Status
101, Rahul Sharma, 2025-09-11, 09:05, Present
102, Priya Singh, 2025-09-11, 09:07, Present
103, Manish Sharma, 2025-09-11, 09:10, Present

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🔒 Security Features

• Password hashing (bcrypt) 🔑
• Role-based access control 🧑‍💻
• Input validation & CSRF protection 🛡️
• Data encryption for facial embeddings 🔐
• Logging & monitoring for admin audit 📜

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🧪 Testing

• ✅ Unit Testing → Individual modules
• 🔄 Integration Testing → Face recognition + DB + UI
• 🌐 System Testing → End-to-end workflow
• 🐞 Debugging Report maintained

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📜 Limitations

• Requires good lighting conditions 💡
• Accuracy decreases with poor camera quality 📷
• Limited by hardware performance ⚙️
• Privacy/legal concerns around biometric data ⚖️

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🔮 Future Enhancements

• 📱 Mobile app (Android/iOS) integration
• 🌐 Cloud database (Firebase/AWS RDS)
• 🤖 AI-based engagement & emotion detection
• 🔑 Multi-Factor Authentication (Face + OTP)
• 🎯 Integration with IoT smart classrooms

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📌 Applications

• 🏫 Schools & Colleges → Student attendance
• 🏢 Offices → Employee time tracking
• 🎤 Events → Entry management
• 🏥 Healthcare → Staff attendance logging
• 🛂 Security → Access control in sensitive areas

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👨‍💻 Author

Manish Sharma
📍 Ghaziabad, Uttar Pradesh, India
📞 +91 7982682852
📧 manishsharma93155@gmail.com
🔗 LinkedIn
💻 GitHub
🌐 Portfolio

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📜 License

This project is licensed under the MIT License – feel free to use, modify, and distribute with proper attribution.

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⭐ Contributing

Contributions, issues, and feature requests are welcome!

1. Fork the repo
2. Create your feature branch (git checkout -b feature-name)
3. Commit changes (git commit -m 'Added new feature')
4. Push branch (git push origin feature-name)
5. Open Pull Request

If you like this project, please ⭐ the repository 🙌