ML-Based Face Recognition System for marking Attendance in organizations

In this paper, we propose a Python-based face recognition system aimed at automating student attendance tracking. Leveraging machine learning techniques, particularly facial recognition algorithms. The system not only registers attendance but also aims to associate detected faces with corresponding student names. Our approach involves the following key steps: first, we capture facial images from the camera feed; second, we preprocess these images to enhance feature extraction and improve recognition accuracy. This feature enables the program to maintain a comprehensive record of attendance, making it invaluable for educational institutions seeking efficient attendance management solutions. By leveraging the versatility of Python and the robustness of machine learning techniques, our aim is to develop a sophisticated yet user friendly application that simplifies attendance tracking while paving the way for future enhancements and innovations in this domain.

Authors

Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/gccit63234.2024.10862388
Citations
5
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
1.25
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article

ML-Based Face Recognition System for marking Attendance in organizations

Munugapati Bhavana, P. Subhashini, Gagan Kumar Koduru, Koppula Srinivas Rao et al.
5 citations
Face recognition and analysis
1.25
article

ML-Based Face Recognition System for marking Attendance in organizations

Munugapati Bhavana, P. Subhashini, Gagan Kumar Koduru, Koppula Srinivas Rao, Vijayasri Nidadavolu, Srividya Mudunuru
article en
5 citations

Abstract

In this paper, we propose a Python-based face recognition system aimed at automating student attendance tracking. Leveraging machine learning techniques, particularly facial recognition algorithms. The system not only registers attendance but also aims to associate detected faces with corresponding student names. Our approach involves the following key steps: first, we capture facial images from the camera feed; second, we preprocess these images to enhance feature extraction and improve recognition accuracy. This feature enables the program to maintain a comprehensive record of attendance, making it invaluable for educational institutions seeking efficient attendance management solutions. By leveraging the versatility of Python and the robustness of machine learning techniques, our aim is to develop a sophisticated yet user friendly application that simplifies attendance tracking while paving the way for future enhancements and innovations in this domain.

Openalex Percentile: Top 18%
Face recognition and analysis
1.25
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