Authors: Jasbani Kaur, Harsh Saxena, Er. Sarika Singh
DOI Link: https://doi.org/10.22214/ijraset.2024.62472
Certificate: View Certificate
Our project aims to revolutionize the conventional attendance system by implementing a facial recognition-based solution. The existing manual methods are prone to human error and require significant maintenance. By leveraging facial recognition technology, our system will offer improved precision and efficiency, reducing the need for manual work. The system will maintain a database of students\' images, matching them during class to mark attendance accurately. Utilizing machine learning techniques, specially the Haar-Cascade classifier and local binary pattern histogram method for face detection and recognition, respectively, our system will ensure reliable attendance tracking. The attendance data will be stored in a MySQL database and Microsoft Excel file, providing a streamlined and secure alternative to the traditional attendance process.
I. INTRODUCTION
In educational institutions and organizations, monitoring attendance is essential for various purposes. While traditional methods like paper-based systems exist, modern advancements have introduced automated solutions, including biometric techniques. One such advanced method is facial recognition technology, a computerized biometric software that verifies individuals by analyzing their facial features. Over the years, facial recognition systems have evolved significantly and found applications in security and various commercial operations. This project focuses on utilizing facial recognition technology for attendance tracking, a practical and efficient solution. As institutions or organizations grow, managing attendance becomes increasingly complex. This project addresses these complexities by automating the attendance tracking process. It involves counting and identifying students or employees in each setting and maintaining accurate attendance records. This project aims to simplify attendance management for institutions and organizations.
II. LITERATURE SURVEY
A real time facial recognition and tracking system personnel presence proposes that the system is based on face detection and recognition algorithm which detects the student face when he/she come in front of camera and then compare the face with the images stored in the data folder if the match is found it will mark the attendance. This system surpasses the traditional system as it saves time also there is no chance of proxy. Here is a detailed summary of some of the existing research studies on this topic:
The system is implemented using Python Django Framework, Haar cascade classifiers, and the LBPH Algorithm for higher accuracy.
4. In their survey paper titled “Face Detection and Recognition Using OpenCV” R.Hasan (2021) highlights the significant role of OpenCV in face detection and recognition. It discusses popular algorithms used in OpenCV for these tasks and explains the OpenCV modules, focusing on Python based implementations. Additionally, it explores various applications of OpenCV. Furthermore, the paper assesses and compares recent literature reviews that utilize OpenCV for human face detection and recognition across different elds. These applications aim to enhance human life through improved technology.
5. Archana (2022) introduce in their research paper titled “Real time Face Detection and Optimal Face Mapping for Online Classes” a web-based tool for real-time face recognition using Convolutional Neural Networks (CNN) and Local Binary Patterns Histograms (LBPH). The study reveals that CNN achieves an impressive accuracy of 95%, while LBPH lags at 78%. Additionally, leveraging a larger dataset of faces in diverse contexts could further improve identification accuracy. Integration with NoSQL/SQL databases could enhance model training performance.
III. PROBLEM STATEMENT
A Real time Facial recognition and tracking system’s primary objective is to modernize the way attendance is recorded in educational institutions by introducing a smart system based on face recognition technology. This system aims to replace the traditional, time consuming method of manual attendance tracking with an automated, efficient, and secure process. Our goals are to enable institutions to effortlessly add new students, ensure accurate face recognition, accommodate various classes or subjects, and automatically maintain attendance records while generating detailed reports. In essence, we are creating a system that simplifies attendance management, making it quicker and digitally accessible for educational organizations.
IV. PROPOSED METHODOLOGY
The process involved in face recognition are: 1. Capture 2. Extraction 3. Comparison 4. Matching The operation in each process is: In step one the capture is the way to snap the picture during the enrolment of the system. Then in the Face Recognition step, extraction is used for finding or extract the specific feature from the face. The third step is comparison, where new input is used for comparison with the database (sample data). Finally, the last step is matching: the system will try to find the matching of the new face with the registered face based on extraction and comparison process. Below mentioned is the proposed methodology that contains data collection, training the classifiers, face detection and face recognition.
This paper introduces a facial recognition system to address the inefficiencies of attendance tracking. With its accuracy, efficiency, and digital accessibility, it has the potential to transform attendance management across educational institutions. The system has used Open CV face recognition method accessible for managing attendance. The system is implemented using the Haar- cascade and LBPH algorithm. LBPH surpasses other algorithms by confidence factor of 2-5 also has least noise interference. The implementation of the A Real Time Facial Recognition and Tracking System for Personnel Presence portrays the existence of an agreement between the appropriate recognition rate and the threshold value. Therefore, LBPH is considered the most authentic and competent face recognition algorithm found in Open CV for the face recognition. A real-time face recognition attendance system is a powerful tool that simplifies attendance management with accuracy and efficiency. To succeed, organizations must prioritize data protection, adapt to evolving regulations, and ensure responsible use. When thoughtfully implemented, this system streamlines attendance processes, enhancing overall operational efficiency.
[1] Bussa, S., Mani, A., Bharuka, S., & Kaushik, S. (2020). Smart attendance system using OPENCV based on facial recognition. Int. J. Eng. Res. Technol, 9(3), 54-59. [2] Manjula, D. A., Kalpana, D., & Guguloth, S. (2023). Facial Recognition Attendance Monitoring System using Deep Learning Techniques. International Journal for Innovative Engineering & Management Research, 12(3). [3] Muthumari, A., Sanu, P. L., Priya, D. B., & Raj, S. M. (2020). FACE RECOGNITION AUTOMATED ATTENDANCE MANGEMENT SYSTEM USING MACHINE LEARNING ALGORITHM. [4] Hasan, R. T., & Sallow, A. B. (2021). Face Detection and Recognition Using OpenCV. Journal of Soft Computing and Data Mining, 2(2), 86-97. [5] Archana, M. C. P., Nitish, C. K., & Harikumar, S. (2022). Real time face detection and optimal face mapping for online classes. In Journal of Physics: Conference Series (Vol. 2161, No. 1, p. 012063). IOP Publishing. [6] Pote, M. C., Somkuwar, P., Raut, K., Chambare, Y., & Borkar, N. (2022). Face Recognition Based Attendance System. In International Journal for Research Publication and Seminar (Vol. 13, No. 3, pp. 23-27).
Copyright © 2024 Jasbani Kaur, Harsh Saxena, Er. Sarika Singh. 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.
Paper Id : IJRASET62472
Publish Date : 2024-05-21
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here