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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM50762

Automated Attendance System Based on Face Recognition Using Deep Learning Techniques

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM50762

Volume:

9 |

6 |

Jun 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem50762

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

MSS

Ms. Suru Sirishma

MIDK

Mr. I. Dillep Kumar

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Abstract

Effective attendance is essential to the education process. Teachers and school administrators need to know how much time each student spends in the classroom. Because of this, many schools use automated attendance systems (AASs). These systems save time and space by enabling schools to register and track students without the need for physical identification cards or teachers. To keep track of students, teachers can use smart cards that monitor attendance records. Biometric technology allows schools to verify student attendance by comparing their biographical data to student identification card entries. A good attendance system helps school administrators maintain proper attendance records for each child. However, no system works effectively when only some required IDs are present among the school’s student population. Implementing AASs requires proper planning and implementation- but it will undoubtedly save time and space in your school’s classroom when properly executed and utilized.

KEYWORDS

Attendance System Face Recognition Machine Learning AASs Automated Attendance

Cite This Article

Ms. Suru Sirishma, Mr. I. Dillep Kumar, "Automated Attendance System Based on Face Recognition Using Deep Learning Techniques," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 6, Jun 2025. https://doi.org/10.55041/ijsrem50762

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Publication Date

6/18/2025

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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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