Face Recognition with Attendance-Based System

Overview

This project is a real-time face recognition-based attendance system built using OpenCV, Haar Cascade, and KNN classifiers. It efficiently automates the attendance process by recognizing faces and storing the data in a structured format.

Features

• Real-Time Face Recognition: Uses OpenCV for accurate detection and recognition.
• Automated Attendance Marking: Records data in a CSV file with timestamps.
• Audio Feedback: Confirms successful attendance marking.
• Efficient Data Preprocessing: Facilitates quick and scalable face dataset management.

Technologies Used

• OpenCV: For face detection and recognition.
• Haar Cascade Classifier: For detecting faces in images.
• KNN Classifier: For classifying facial features.
• Python: The core programming language.

Implementation

1. Face Detection: Captures real-time images using a webcam and detects faces using the Haar Cascade classifier.
2. Feature Extraction: Extracts features for training using the KNN algorithm.
3. Attendance Marking: Matches detected faces with the dataset and marks attendance in a CSV file.

Code Snippet

# Import required libraries
import cv2
import numpy as np

# Initialize Haar Cascade for face detection
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load webcam feed
cap = cv2.VideoCapture(0)
while True:
    _, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
    cv2.imshow('Face Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

Repository

Check the full implementation on GitHub: 
https://github.com/krisharora7/Face-Recognition-with-Attendance-Based-system

Applications

Educational Institutions: Automates student attendance.

Corporate Offices: Tracks employee attendance.

Event Management: Simplifies participant verification.


Future Scope

Integration with cloud storage for scalability.

Enhanced recognition using deep learning models.

Support for multi-camera environments.



Tags: #FaceRecognition #OpenCV #AutomatedAttendance #Python #HaarCascade #KNNClassifier #ComputerVision #AI