Face Recognition Attendance System

Face Recognition Attendance System

Project Overview

Face recognition is a common problem in artificial intelligence. This feature is used a lot in our daily life. For example, many smartphones unlock using face ID, and Facebook can recognize people in photos automatically. There are many ways to do face recognition, but it’s still hard to get perfect results in real-life situations. This is because of things like lighting, face angles, or when part of the face is covered. This project focuses on building a face recognition system using machine learning and PCA (Principal Component Analysis). It was tested with different methods like Linear Discriminant Analysis, Multilayer Perceptron, Naive Bayes, and Support Vector Machine. The results showed good accuracy — 97% to 100% using PCA and LDA.

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Category: Web Application
Developer: UPDATEGADH
Languages Used: Python, HTML, Bootstrap
Framework: Flask
Database: No database integrated (uses file system-based storage)
Libraries/Tools: OpenCV, Dlib, Haar Cascade Classifier

Project Information Table

Available Features

Add New User
Upload and register a new user’s face into the system.

Capture Live Images
Capture real-time images using webcam and store them for model training.

User Count Tracking
View the total number of registered users.

Take Attendance
Trigger live face recognition to mark attendance based on detected faces.

Multiple Face Detection
Recognizes and processes multiple faces at once in real-time.

Attendance Recording
Records and saves attendance with timestamp.

Display Attendance
View the current day’s attendance with user details.

 Technologies Used

• Frontend: HTML5, Bootstrap
• Backend: Python (Flask Framework)
• Face Detection: OpenCV (Haarcascade)
• Face Recognition: Dlib
• Storage: File-system based (CSV)

 Project Structure

├── app.py                      # Main Flask Application
├── Attendance/                 # Attendance CSV records
├── static/                     # Static files (CSS, JS, images)
├── templates/                  # HTML Templates (Bootstrap-based)
├── haarcascade_frontalface.xml# Face detection model
├── requirements.txt            # Python dependencies
└── README.md                   # Documentation

 How It Works

1. Start Flask App via python app.py
2. Navigate to http://127.0.0.1:5000/
3. Register new users by capturing images
4. Click Take Attendance
5. Attendance is stored automatically in the /Attendance folder as .csv files.