🎯 Smart Attendance System with Face Recognition (LBPH)

A real-time face recognition-based attendance system built using Computer Vision to automate and improve the accuracy of attendance tracking.

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📌 Project Overview

This project implements an automated attendance system using face recognition with the LBPH (Local Binary Patterns Histogram) algorithm.

The system captures facial data, trains a recognition model, and records attendance in real-time using a webcam—eliminating the need for manual input and reducing human error.

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🎯 Objective

• Automate attendance recording using face recognition
• Reduce fraud such as proxy attendance
• Improve efficiency and data accuracy
• Apply computer vision concepts in a real-world use case

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✨ Key Features

• 🎥 Real-time face detection
• 🧠 Face recognition using LBPH algorithm
• 📸 Face dataset collection
• ⚙️ Model training pipeline
• ✅ Automated attendance recording
• 💾 Export attendance data to CSV / Excel
• 🖥️ GUI interface using Tkinter

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🧠 System Workflow

1. Input user data (Name, ID, Class)
2. Capture facial dataset via webcam
3. Train recognition model using LBPH
4. Detect and recognize faces in real-time
5. Automatically record attendance

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📸 Preview

GUI interface and attendance output generated by the system.

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🛠️ Tech Stack

• Python
• OpenCV
• NumPy
• Pandas
• Tkinter (GUI)

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📂 Project Structure

Smart-Absensi/
├── dataset/          # Data wajah
├── trainer/          # Model hasil training
├── images/           # Screenshot / dokumentasi
├── main.py           # Program utama
├── requirements.txt  # Dependency
└── README.md         # Dokumentasi project

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⚙️ Installation

git clone https://github.com/imammularif/SMART-ABSENSI-WITH-FACE-RECOGNITION-LBPH-USING-PYTHON.git cd SMART-ABSENSI-WITH-FACE RECOGNITION-LBPH-USING-PYTHON pip install -r requirements.txt

▶️ Run the Application

python main.py

📊 Output

The system generates attendance records in:

• CSV
• Excel

⚠️ Requirements

• Active webcam
• Adequate lighting conditions
• Clear facial visibility during training

🚀 Future Improvements

• Improve GUI/UX design
• Integrate database (MySQL / PostgreSQL)
• Develop web-based version (Flask / Django)
• Add dashboard for attendance monitoring
• Support multi-user system

💡 Reflection

• This project allowed me to apply computer vision concepts into a practical system. I learned how to build a pipeline from data collection to model training and real-time implementation.

📢 License

• This project is open-source and intended for educational purposes.