Face Recognition Attendance System

Authors

  • Goel P Dept. of Computer Science, Dr. Akhilesh Das Gupta Institute of Technology & Management, Delhi, India
  • Abdul Aleem Ansari Dept. of Computer Science, Dr. Akhilesh Das Gupta Institute of Technology & Management, Delhi, India.
  • Apurva Jain Dept. of Computer Science, Dr. Akhilesh Das Gupta Institute of Technology & Management, Delhi, India
  • Anshul Nagar Dept. of Computer Science, Dr. Akhilesh Das Gupta Institute of Technology & Management, Delhi, India

DOI:

https://doi.org/10.26438/ijcse/v11i12.5355

Keywords:

AI-Driven Resource Optimization, Serverless Computing, Fault Prediction, Deep Reinforcement Learning and Ensemble Methods

Abstract

In today’s digital era, face recognition systems have emerged as a vital component across various sectors. As one of the most commonly used biometric technologies, face recognition offers a multitude of advantages including security, authentication, and identification. Despite its lower accuracy compared to iris recognition and fingerprint recognition, face recognition continues to gain traction due to its contactless and non-invasive nature. Moreover, it can be effectively utilized for attendance marking in educational institutions and workplaces, addressing the shortcomings of traditional manual methods such as consumption and the risk of proxy attendance. This paper proposes a class attendance system based on face recognition, aiming to streamline the attendance process and eliminate its associated challenges. The system encompasses four key phases: database creation, face detection, face recognition, and attendance updation. The database is created by capturing images of students in the classroom. Face detection and recognition are performed using the Haar-Cascade classifier and Local Binary Pattern Histogram algorithm, respectively. Faces are detected and recognized in real time using live streaming videos. At the end of each session, the attendance is automatically sent via email to the respective faculty.

References

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[5] Rathod, Hemantkumar, et al. "Attendance system using machine learning approach." 2017 (ICNTE).IEEE, 2017.

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[9] Salim,Omar Abdul Rehman, Rashidah Funke Olanrewaju, and Wasiu Adebayo Balogun. "Class attendance management system using face recognition." 2018.

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Published

2023-12-31
CITATION
DOI: 10.26438/ijcse/v11i12.5355
Published: 2023-12-31

How to Cite

[1]
P. Goel, A. A. Ansari, A. Jain, and A. Nagar, “Face Recognition Attendance System”, Int. J. Comp. Sci. Eng., vol. 11, no. 12, pp. 56–60, Dec. 2023.

Issue

Vol. 11 No. 12 (2023): IJCSE December Edition

Section

Research Article

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