Automated Facial Recognition based Attendance System using OpenCV in Python

Authors

  • Avoy Sain  
  • Samrat Dutta  
  • Rimpi Saha  
  • Unmesh Mandal  

DOI:

https://doi.org/10.32628/CSEIT2390617

Keywords:

Face Detection, Face Recognition, Automated Attendance System, Open CV, Python.

Abstract

Face detection and recognition systems work by detecting faces present in an image or in a video frame and identifying the person in the image. In this work, we are interested in face detection to achieve an automatic attendance system. This work is implemented using Python and can be operated from any standalone device. This automated system stores the attendance records of students/employees with proper timestamps in the local Secondary Memory. All these records are stored date-wise. The implementation of an Automated Facial Recognition based Attendance System can help in identifying and verifying a person’s identity from a digital source in real-time. Accurate attendance records are very important for classroom evaluation in schools and colleges. It also helps to keep track of the attendance of the employees in different organizations. The traditional system of manual attendance tracking in institutes can result in errors and missed or duplicated entries. The adoption of the Face Recognition-based attendance system could help eliminate these problems and shortcomings of the classical manual attendance system. The proposed work is tested with different persons with different age groups and genders in real time. The system successfully identifies the proper persons with significant accuracy and records their attendance.

References

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Published

2023-12-30

Issue

Section

Research Articles

How to Cite

[1]
Avoy Sain, Samrat Dutta, Rimpi Saha, Unmesh Mandal, " Automated Facial Recognition based Attendance System using OpenCV in Python" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.105-111, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT2390617