A Deep Learning-Based Face Recognition Attendance System

Abstract

Facial Recognition is a technology that has been used in many areas like security systems, human machine interaction and image processing techniques. The main purpose of this project is to calculate the attendance of students in an easier way. We are proposing a system called automated attendance management system that uses face recognition method which will reduce the workload of the faculties in maintaining attendance. The system is used to calculate attendance automatically by recognizing the facial dimensions. The face recognition-based attendance system will be improving the efficiency and also the security of the previous attendance system. Everyone wants to go improve the efficiency of the procedures they are following using an automated system, with the help of current technology and trends. Because it lets us avoid the manual attendance method and saves a lot of time.

Introduction

The text describes the development of an automated attendance system using face recognition to replace traditional manual attendance methods, which are time-consuming and prone to proxy attendance. Existing biometric systems like RFID, fingerprint, and iris recognition are discussed, but they are often slow, intrusive, or inconvenient. Face recognition is preferred because it is contactless, non-intrusive, and efficient for real-world classroom environments.

The background study shows the evolution of face recognition techniques, from traditional feature-based machine learning methods to advanced deep learning models. Accuracy has improved over time, with deep learning approaches like DCNN achieving higher performance, though challenges such as lighting, pose variation, and facial expression changes still affect results. The proposed FACELOG system uses a multi-pose CNN approach to improve robustness.

The proposed system works by first registering student faces into a dataset. During class, live video is captured, faces are detected using OpenCV (Haar Cascade), and matched against the dataset to automatically mark attendance and identify absentees. The system consists of four main stages: face detection, dataset creation, recognition, and attendance marking.

Face detection uses grayscale conversion and Haar Cascade classifiers to locate faces in real time. Dataset creation involves capturing and labeling images using OpenCV and storing them for training a supervised learning model.

Conclusion

The facial recognition attendance system proved to be a successful and efficient way of tracking attendance. The system was able to accurately recognize and identify individuals in a timely manner, saving time and reducing errors compared to traditional attendance tracking methods. The implementation of a GUI also made the system user-friendly and easy to operate.

References

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Copyright

Copyright © 2026 Prof. Satish C. Cholke, Ms.Tejal Sanjay Jagdale, Ms. Payal Sampat Sangle, Ms. Vaishnavi Suresh Avhad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.