Streamlining Attendance: The Multiface Attendance System Using MTCNN

Streamlining Attendance: The Multiface Attendance System Using MTCNN

Revolutionizing Attendance Management with Facial Recognition: The Multiface Attendance System

Taking attendance has always been a tedious task, whether it's roll calls in classrooms or sign-in sheets in offices. Enter the Multiface Attendance System, a cutting-edge solution that leverages facial recognition to automate attendance management. By using MTCNN (Multi-task Cascaded Convolutional Networks) for face detection and combining it with advanced machine learning techniques, this system offers a fast, accurate, and user-friendly way to log attendance in real-time.
Implementation Overview

Show Image
The image above shows the actual implementation of our attendance system using Python. Key components include:
OpenCV for video capture
YOLO for object detection
CSV handling for attendance records
Datetime integration for timestamp logging

Real-World Usage
The system successfully tracks attendance with:
Individual name recording
Timestamp logging (2024–09 format)
Support for multiple attendees (as shown: Himanshu, Vaibhawi, Shantanu, Yamesh)
Automated CSV file generation

Detection Algorithm Comparison

We conducted extensive research to select the optimal face detection algorithm for our system. Here's how different algorithms compare:
Detection Accuracy Breakdown
HAAR: 70% - Lightweight but less accurate
YOLO: 92% - Excellent for general object detection
Segmentation: 90% - Strong pixel-level accuracy
YOLO Faces: 80% - Specialized but less accurate than standard YOLO
MTCNN: 96% - Superior face detection capabilities
Face Recognition Libraries: 95%+ for recognition tasks

Based on this analysis, we chose MTCNN for our system due to its:
Highest detection accuracy (96%)
Specialized capability for small or occluded faces
Robust performance in various lighting conditions

How It Works

1. Data Collection The system begins by gathering facial data using a webcam. A script prompts users to input their names, which it then associates with their captured face images. These images are stored in a directory for further processing.

2. Model Training
   The training process involves two key steps:
   Embedding Extraction: The FaceNet model extracts 128-dimensional facial embeddings from the collected images.
   Classifier Training: An SVM (Support Vector Machine) classifier learns to distinguish faces based on these embeddings. Once trained, the model is saved for real-time recognition.

3. Real-Time Recognition

Using MTCNN, the system detects faces in a live video feed. The embeddings of detected faces are classified by the trained SVM model, and attendance is logged in a CSV file with a timestamp.
Technical Implementation Details

Core Components

import cv2
import csv
from ultralytics import YOLO
from datetime import datetime

Load your trained YOLOv8 model

model = YOLO('best.pt') # Replace with the path to your trained YOLOv8 model

Set up video capture

cap = cv2.VideoCapture(0) # Replace with video file if needed

Set up attendance tracking

attendance_set = set() # To ensure each object is recorded only once

CSV file to save attendance

csv_file = 'attendance.csv'
Future Enhancements
While the system is highly functional, there are several exciting possibilities for growth:
Cloud Connectivity
Centralized storage of attendance data for easy access and analysis, enabling remote monitoring and management.
Enhanced Security
Incorporating encryption and additional verification methods to protect sensitive information and ensure data privacy.
Conclusion
The Multiface Attendance System demonstrates how modern technology can revolutionize everyday processes. By combining MTCNN's 96% detection accuracy with machine learning and a simple interface, this system offers a glimpse into the future of attendance management. Whether in schools, offices, or events, it sets the standard for efficiency and reliability.