Face Recognition-Based Attendance Management System

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Overview

The Face Recognition-Based Attendance Management System is a real-time computer vision application that automates attendance using facial recognition. The system captures facial features through a webcam, identifies registered users using a K-Nearest Neighbors (KNN) classifier, and records attendance with timestamps in CSV format.

Developed using Python and OpenCV, the project eliminates manual attendance processes while providing a simple and efficient solution for classrooms and organizations.

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Key Features

• Real-time face detection using OpenCV
• Face recognition using K-Nearest Neighbors (KNN)
• Automatic attendance recording
• Face dataset creation for new users
• Timestamped attendance logs
• Persistent face data storage using Pickle
• User-friendly interface with webcam integration

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Technologies Used

Category	Technologies
Programming	Python
Computer Vision	OpenCV
Machine Learning	K-Nearest Neighbors (KNN)
Data Processing	NumPy
Data Storage	Pickle
Output	CSV

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System Workflow

User Registration
        │
        ▼
Face Image Capture
        │
        ▼
Feature Extraction
        │
        ▼
Store Face Embeddings
        │
        ▼
Real-Time Webcam Feed
        │
        ▼
Face Detection
        │
        ▼
KNN Face Recognition
        │
        ▼
Attendance Verification
        │
        ▼
CSV Attendance Log

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Project Features

User Registration

• Capture multiple face samples
• Store facial data securely
• Associate captured faces with user names

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Face Recognition

The application continuously captures frames from the webcam and:

• Detects faces using OpenCV Haar Cascade
• Extracts facial features
• Predicts the user's identity using KNN

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Attendance Management

Once a face is recognized, the system:

• Records the user's name
• Captures the current date and time
• Saves attendance in CSV format
• Prevents duplicate attendance entries

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Project Structure

Face-Recognition-Attendance/

│── add_faces.py
│── test.py
│── requirements.txt
│── README.md
│
├── data/
│   ├── haarcascade_frontalface_default.xml
│   ├── names.pkl
│   └── faces_data.pkl

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Installation

Clone the repository

git clone https://github.com/harshitha923/Attendance-Management.git

Install dependencies

pip install -r requirements.txt

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Running the Project

Step 1 – Register a User

python add_faces.py

The application captures multiple face samples and stores them for future recognition.

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Step 2 – Start Attendance System

python test.py

The webcam starts automatically.

When a registered face is detected:

• Attendance is recorded
• Timestamp is generated
• CSV file is updated

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Sample Output

Attendance log contains:

Name	Date	Time
John Doe	19-06-2025	09:15:32

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Future Improvements

• Deep Learning-based Face Recognition (FaceNet / ArcFace)
• Anti-spoofing detection
• Multi-face recognition
• Cloud database integration
• Web dashboard
• Attendance analytics

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Author

Harshitha

M.Tech Information Technology

Areas of Interest

• Computer Vision
• Machine Learning
• Artificial Intelligence
• Deep Learning
• Data Science

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