Employee attendance system using face recognition and GPS using local binary pattern histogram
Narahari Vigraha Prasada & Ikrimach
Abstract
Tracking employee attendance is an integral part of running a company in an organized and economical manner. Conventional approaches such as manual sign-ins and RFID cards or fingerprint scanning have shown important weaknesses, especially with regard to proxy attendance (buddy punching). We chose the LBPH algorithm since it has a higher flexibility against changes of light, which means that we can use it in many situations like indoor or outdoor cases. The system performances for various conditions were also noteworthy, achieving 96.4% recognition accuracy with FAR = 0.05 %, FRR = 1 % in normal lighting conditions and maintaining a 94.1 % near-accurate performance under low-light environmental settings whilst sustaining the performance at 90.6 % in outdoor environments, which resulted in detection time of approximately between 1.3–2.3 seconds respectively. For further peace of mind, the system incorporated GPS tracking to provide location verification with a 90% to 94% accuracy rate—logging attendance only when students were present in a designated area. This integrated system is especially useful in contemporary hybrid workplaces, as it minimizes attendance fraud and enhances operational efficiency. Although the system is capable of functionally robust performance under normal conditions, tests point to possible scalability and performance improvements in extreme lighting conditions and outdoor applications, thus establishing future development paths for environmental adaptation.
Keywords
face recognition, GPS tracking, employee attendance system, local binary pattern histogram (LBPH)
Author information & Contribution
Narahari Vigraha Prasada. Corresponding author. University of Technology Yogyakarta, Yogyakarta, Indonesia. E-mail: vigrahanarahari@gmail.com
Ikrimach. University of Technology Yogyakarta, Yogyakarta, Indonesia. Email: ikrimach@uty.ac.id
“All authors equally contributed to the conception, design, preparation, data gathering and analysis, and writing of the manuscript. All authors read and approved the final manuscript.”
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was not supported by any funding.
Institutional Review Board Statement
Data and Materials Availability
AI Declaration
Notes
Acknowledgement
References
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Cite this article:
Prasada, N.V. & Ikrimach (2024). Employee attendance system using face recognition and GPS using local binary pattern histogram. International Journal of Science, Technology, Engineering and Mathematics, 4(4), 83-107. https://doi.org/10.53378/ijstem.353133
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