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
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
AI Declaration
Notes
Acknowledgement
References
Baig, S., Geetadhari, K., Noor, M. A., & Sonkar, A. (2022). Face recognition based attendance management system by using machine learning. International Journal of Multidisciplinary Research and Growth Evaluation, 1–4. https://doi.org/10.54660/anfo.2022.3.3.1
Charpignon, M.-L., Yuan, Y., Zhang, D., Amini, F., Yang, L., Jaffe, S., & Suri, S. (2023). Navigating the new normal: Examining coattendance in a hybrid work environment. Proceedings of the National Academy of Sciences, 120(51). https://doi.org/10.1073/pnas.2310431120
Habu, R., Motade, S., Kukade, S., Gunale, K., & Nair, A. (2022). Smart face recognition based attendance system using ML algorithm. 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 527–532. https://doi.org/10.1109/ICAC3N56670.2022.10074166
Hasan, R., Islam, S., Rahman, Md. H., Saifuzzaman, Mohd., Shetu, S. F., & Moon, N. N. (2020). Implementation of low cost real-time attendance management system: A comparative study. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1098–1101. https://doi.org/10.1109/ICRITO48877.2020.9197764
Jabez, J., Keerthanaa, V., Kaviya, V., & Gowri, S. (2020). An enhanced web based attendance application using global positioning system and face recognition. Journal of Computational and Theoretical Nanoscience, 17(8), 3344–3348. https://doi.org/10.1166/jctn.2020.9183
Joshi, D., Patil, P., Singh, V., Vanjari, A., Shinde, T., & Giri, H. (2023). Face recognition based attendance system. 2023 5th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), 1–6. https://doi.org/10.1109/ICNTE56631.2023.10146718
Khuran, A., Lohani, B. P., Bibhu, V., & Kushwaha, P. K. (2021). An AI integrated face detection system for biometric attendance management. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), 29–33. https://doi.org/10.1109/ICIEM51511.2021.9445295
Kumar, A., Sharma, M., Gautam, S. P., Kumar, R., & Raj, S. (2020). Attendance management system using facial recognition. 2020 International Conference on Decision Aid Sciences and Application (DASA), 228–232. https://doi.org/10.1109/DASA51403.2020.9317104
Kumar, A., & Singh, D. (2023). Comprehensive approach of real time web-based face recognition system using Haar Cascade and LBPH algorithm. 2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT), 371–376. https://doi.org/10.1109/DICCT56244.2023.10110049
Lavanya, P., Lavanya Devi, G., & Srinivasa Rao, K. (2021). LBPH-based face recognition system for attendance management. In: Chowdary, P., Chakravarthy, V., Anguera, J., Satapathy, S., Bhateja, V. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 655. Springer, Singapore. https://doi.org/10.1007/978-981-15-3828-5_6
Liu, Y., Chen, L., Ou, Z., Chen, J., & Wu, J. (2020). A crowdsourcing based multi-modal attendance tracking system for smartphone users. 2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), 61–64. https://doi.org/10.1109/ISCEIC51027.2020.00021
Meden, B., Rot, P., Terhorst, P., Damer, N., Kuijper, A., Scheirer, W. J., Ross, A., Peer, P., & Struc, V. (2021). Privacy–enhancing face biometrics: A comprehensive survey. IEEE Transactions on Information Forensics and Security, 16, 4147–4183. https://doi.org/10.1109/TIFS.2021.3096024
Menezes, T. (2021). Face recognition attendance system using raspberry pi. International Journal for Research in Applied Science and Engineering Technology, 9(8), 1145–1149. https://doi.org/10.22214/ijraset.2021.37499
Mohan Katta, L. (2023). Face recognition attendance system using LBPH algorithm. International Journal of Scientific Research, 52–53. https://doi.org/10.36106/ijsr/3556592
Mukherjee, K., Manish, K. M. M., & Natrajan, G. (2022). Employee attendance system based on facial recognition. International Journal of Health Sciences, 5054–5069. https://doi.org/10.53730/ijhs.v6nS5.9736
Pabanaas, V., Singhal, S., Saxena, A., Chatterjee, J., & Mehra, A. (2023). Analysis of CPU utilization of a cross-platform web application for facial recognition based remote user tracking system. 2023 3rd International Conference on Intelligent Technologies (CONIT), 1–5. https://doi.org/10.1109/CONIT59222.2023.10205762
Pilania, U., & Singh, S. (2022). Implementation of image-based attendance system. 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), 937–941. https://doi.org/10.1109/ICESC54411.2022.9885713
Pooja, G. (2023). Face recognition based attendance system. International Journal for Research in Applied Science and Engineering Technology, 11(5), 5408–5412. https://doi.org/10.22214/ijraset.2023.52868
Priya, R. L., Nanda, S., Dandekar, M., Devnani, A., & Kadakoti, R. (2021). Face-recognition based attendance system using one-shot learning. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 01–07. https://doi.org/10.1109/ICCCNT51525.2021.9579899
Smitha, Pavithra S Hegde, & Afshin. (2020). Face recognition based attendance management system. International Journal of Engineering Research and Technology, 9(05). https://doi.org/10.17577/IJERTV9IS050861
Sri Harish, J., Muthu Revanth, M. & Bharathi, B.J. (2021). Cloud-based attendance application using face recognition. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), 881–890. https://doi.org/10.1109/ICOEI51242.2021.9453042
Srivastava, M., Kumar, A., Dixit, A., & Kumar, A. (2020). Real time attendance system using face recognition technique. 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and Its Control (PARC), 370–373. https://doi.org/10.1109/PARC49193.2020.236628
Tej Chinimilli, B., T., A., Kotturi, A., Reddy Kaipu, V., & Varma Mandapati, J. (2020). Face recognition based attendance system using Haar cascade and local binary pattern histogram algorithm. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), 701–704. https://doi.org/10.1109/ICOEI48184.2020.9143046
Thomas, R. (2020). Real-time classroom attendance monitoring system based on face recognition. International Journal of Information Systems and Computer Sciences, 9(3), 16–20. https://doi.org/10.30534/ijiscs/2020/02932020
Uddin, K. M. M., Chakraborty, A., Hadi, Md. A., Uddin, M. A., & Dey, S. K. (2021). Artificial intelligence based real-time attendance system using face recognition. 2021 5th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 1–6. https://doi.org/10.1109/ICEEICT53905.2021.9667836
Wati, V., Kusrini, K., Al Fatta, H., & Kapoor, N. (2021). Security of facial biometric authentication for attendance system. Multimedia Tools and Applications, 80(15), 23625–23646. https://doi.org/10.1007/s11042-020-10246-4
Yadav, A., Sharma, A., & Yadav, S. S. (2022). Attendance management system based on face recognition using Haar-Cascade. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 1972–1976. https://doi.org/10.1109/ICACITE53722.2022.9823613
Zhao, C., & Huang, X. (2020). Attendance system based on face recognition and GPS tracking and positioning. 2020 2nd International Conference on Applied Machine Learning (ICAML), 78–83. https://doi.org/10.1109/ICAML51583.2020.00024
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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