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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM37266

Face Recognition-Based Attendance System Using Python: A Novel Approach to Automated Attendance Management

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM37266

Volume:

8 |

8 |

Aug 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM37266

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SS

Shaheer S

AV

Adarsh V

AA

Afsal Ansari

SS

Salman Sunil

NA

Nishil A

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Abstract

This study shows a face recognition-based attendance system created with Python. It uses machine learning algorithms to boost accuracy and productivity. Old-school attendance methods often needed to be corrected and took a lot of time. This new system changes that by using real-time facial recognition to do the job. The system relies on a mix of Logistic Regression, Support Vector Machines (SVM), and Random Forest classifiers, with a Voting Classifier to make sure it works as well as possible. The team did a lot of work to clean up facial images and fine-tune settings to make the model more accurate. The results show big improvements in managing attendance, with high accuracy and strong performance in different settings. This system offers a solution that can grow with organizations looking to simplify attendance tracking, reduce human mistakes, and make their operations more effective.

KEYWORDS

Face recognition attendance system machine learning real-time application Voting Classifier Python.

Cite This Article

Shaheer S, Adarsh V, Afsal Ansari, Salman Sunil, Nishil A, "Face Recognition-Based Attendance System Using Python: A Novel Approach to Automated Attendance Management," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 8, Aug 2024. https://doi.org/10.55041/IJSREM37266

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Publication Date

8/30/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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