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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Attendance system based on facial recognition using multi-task convolutional neural network

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  • Attendance system based on facial recognition using multi-task convolutional neural network

Lance Andrew Rollon *, Caye Angelica Lusing, John Bryan Aquino, Criselle Centeno and Ariel Antwaun Rolando Sison

Information Technology Department, Pamantasan ng Lungsod ng Maynila, Manila, Philippines.
 
Review Article
World Journal of Advanced Research and Reviews, 2023, 18(03), 001-006
Article DOI: 10.30574/wjarr.2023.18.3.0971
DOI url: https://doi.org/10.30574/wjarr.2023.18.3.0971
 
Received on 17 April 2023; revised on 27 May 2023; accepted on 30 May 2023
 
Some businesses today do not keep track of their employees' attendance. Some of them are still using outdated techniques such as log-based manual inspections. These outdated methods of data collection require excessive paperwork, take time due to laborious recording procedures, and result in inaccurate data collection due to human error and inadequate assessment. The goal of this study is to create an attendance system that can make it simple and accurate for businesses to track their employees' attendance. The aim of this project is to create a system that will make it simple and automatic to record attendance using facial recognition. The result shows that the system is operational and possess a accurate and highly benefits the students and the faculty members.
 
Attendance System; Face Recognition; Multi-Convolutional Neural Network; Machine Learning
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2023-0971.pdf

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Lance Andrew Rollon, Caye Angelica Lusing, John Bryan Aquino, Criselle Centeno and Ariel Antwaun Rolando Sison. Attendance system based on facial recognition using multi-task convolutional neural network. World Journal of Advanced Research and Reviews, 2023, 18(3), 001-006. Article DOI: https://doi.org/10.30574/wjarr.2023.18.3.0971

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