A Hybrid Image Processing Approach for Real-Time Face Recognition in Attendance Monitoring

Davit Cany  Agho; Aria Hendrawan

doi:10.63158/IJAIS.v2.i1.17

Authors

Davit Cany Agho Universitas Semarang Author
Aria Hendrawan Universitas Semarang Author

DOI:

https://doi.org/10.63158/IJAIS.v2.i1.17

Keywords:

Face Recognition, Attendance Monitoring, OpenCV, Haar Cascade Classifier, LBPH

Abstract

In the era of digital transformation, institutions are increasingly adopting automation to enhance administrative efficiency, particularly in human resource management. At Tanggirejo Village Hall, a critically low employee attendance rate of 46.45% in January 2024 exposed the limitations of manual attendance systems, which are prone to errors and manipulation. This study proposes a face recognition-based attendance system utilizing OpenCV’s Haar Cascade Classifier for face detection and the Local Binary Pattern Histogram (LBPH) for face recognition. A total of 500 grayscale facial images from 10 employees were collected and processed to train and test the system. Evaluation using a Confusion Matrix revealed an accuracy of 72%, precision of 93%, and recall of 75%. While a 27% error rate was observed—primarily due to lighting inconsistencies and limited training data—the system performed reliably in real-time scenarios. The integration of these lightweight algorithms allows for fast and accurate identification, suitable for resource-constrained environments. This solution not only addresses the local attendance challenges but also presents a scalable, automated model that can be adopted by similar institutions seeking to improve productivity and operational transparency through real-time employee monitoring.

Author Biography

Davit Cany Agho, Universitas Semarang

Fakultas Teknologi Informasi dan Komunikasi

References

M. Attatsitsey and N. Osei-Bonsu, “Assessing the impact of information technology on human resource practices: Evidence from organisations in Ghana,” Int. J. Inf. Technol. Manag., vol. 20, no. 1–2, pp. 5–20, 2021, doi: 10.1504/IJITM.2021.114154.

O. Papaevangelou, D. Syndoukas, S. Kalogiannidis, and S. Kontsas, “Information Technology and Human Resource Management in Educational Institutions,” J. Syst. Manag. Sci., vol. 13, no. 2, pp. 258–272, 2023, doi: 10.33168/JSMS.2023.0218.

F. Mizrak, “Use of information technologies in strategic human resource management,” in Enhancing Employee Engagement and Productivity in the Post-Pandemic Multigenerational Workforce, IGI Global, 2023, pp. 332–351, doi: 10.4018/978-1-6684-9172-0.ch017.

M. A. Alaghbari, A. Ateeq, M. Alzoraiki, M. Milhem, and B. A. H. Beshr, “Integrating Technology in Human Resource Management: Innovations and Advancements for the Modern Workplace,” in Proc. 2024 ASU Int. Conf. Emerging Technol. Sustain. Intell. Syst. (ICETSIS), 2024, pp. 307–311, doi: 10.1109/ICETSIS61505.2024.10459498.

L. Micic and V. Radosavac, “Influence of Information Technology to Human Resources Management: Key Trends in 21st Century,” in Lecture Notes in Networks and Systems, vol. 28, Springer, 2018, pp. 271–281, doi: 10.1007/978-3-319-71321-2_25.

N. Kengatharan, “Firm performance: HR practices and IT,” SCMS J. Indian Manag., vol. 17, no. 1, pp. 110–122, 2020.

M. I. M. Rozlan et al., “RFID Based Attendance Monitoring System with LED Authentication,” in Proc. 2023 IEEE Int. Conf. Automat. Control Intell. Syst. (I2CACIS), 2023, pp. 85–90, doi: 10.1109/I2CACIS57635.2023.10193394.

C. Lv, J. J. Zhang, and Y. Ma, “Student attendance management system based on campus smart card platform,” Appl. Mech. Mater., vol. 321–324, pp. 3022–3025, 2013.

N. Karki et al., “Smart Attendance Monitoring System Using Face Recognition for People with Disabilities (PwDs),” in Proc. 2023 IEEE Int. Smart Cities Conf. (ISC2), 2023, doi: 10.1109/ISC257844.2023.10293664.

A. Venugopal, R. R. Krishna, and U. Rahul Varma, “Facial Recognition System for Automatic Attendance Tracking Using an Ensemble of Deep-Learning Techniques,” in Proc. 2021 12th Int. Conf. Comput. Commun. Netw. Technol. (ICCCNT), 2021, doi: 10.1109/ICCCNT51525.2021.9580098.

A. Jha et al., “Raspberry Pi-powered door lock with facial recognition,” in Proc. 2024 IEEE Int. Students’ Conf. Electr., Electron. Comput. Sci. (SCEECS), 2024, doi: 10.1109/SCEECS61402.2024.10481920.

N. Pandey, P. K. Yadav, and K. P. Arjun, “Face Recognition System Using Computational Algorithms,” in Proc. 2022 2nd Int. Conf. Adv. Comput. Innov. Technol. Eng. (ICACITE), 2022, pp. 1739–1744, doi: 10.1109/ICACITE53722.2022.9823568.

A. K. Singh, S. Krishna, and T. Poongodi, “Face Recognition System Using Haar Cascade and LBP Classifier,” in Proc. Int. Conf. Commun., Comput. Sci. Eng. (IC3SE), 2024, pp. 99–104, doi: 10.1109/IC3SE62002.2024.10593491.

K. Ramalakshmi et al., “Facial Recognition System with LBPH Algorithm: Implementation in Python for Machine Learning,” in Proc. 2nd Int. Conf. Intell. Cyber Phys. Syst. Internet Things (ICoICI), 2024, pp. 1681–1686, doi: 10.1109/ICoICI62503.2024.10696268.

A. M. Irfan et al., “Comparative Analysis of Face Recognition Algorithms: Evaluating LBPH and FaceNet with Haar Cascade,” in Proc. 2023 14th Int. Conf. Comput. Commun. Netw. Technol. (ICCCNT), 2023, doi: 10.1109/ICCCNT56998.2023.10307725.

Z. Yang, W. Ge, and Z. Zhang, “Face recognition based on MTCNN and integrated application of FaceNet and LBP method,” in Proc. 2020 2nd Int. Conf. Artif. Intell. Adv. Manuf. (AIAM), 2020, pp. 95–98, doi: 10.1109/AIAM50918.2020.00024.

S. Shilaskar et al., “Robust Criminal Identification System for Recognition of Obscure and Hidden Faces,” in Proc. 2023 2nd Int. Conf. Futurist. Technol. (INCOFT), 2023, doi: 10.1109/INCOFT60753.2023.10425451.

R. R. Isnanto et al., “Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images,” Int. J. Eng. Technol. Innov., vol. 11, no. 1, pp. 45–58, 2021, doi: 10.46604/IJETI.2021.6174.

B. Elkari et al., “Implementation of presence detection with Haar cascade and local binary patterns histograms,” Math. Model. Comput., vol. 11, no. 4, pp. 1093–1105, 2024, doi: 10.23939/mmc2024.04.1093.

W. G. Aguilar et al., “Pedestrian detection for UAVs using cascade classifiers and saliency maps,” in Lecture Notes in Computer Science, I. Rojas, A. Catala, and G. Joya, Eds., Springer, 2017, pp. 563–574, doi: 10.1007/978-3-319-59147-6_48.

K. G. Saravanan et al., “Deep Learning based Facial Recognition System for Attendance Maintenance,” in Proc. Int. Conf. Edge Comput. Appl. (ICECAA), 2022, pp. 1458–1462, doi: 10.1109/ICECAA55415.2022.9936170.

P. Citra et al., “Implementasi Pengolahan Citra Digital dalam Pengenalan Wajah menggunakan Algoritma PCA dan Viola Jones,” Hello World J. Ilmu Komput., vol. 2, no. 3, pp. 146–157, Oct. 2023, doi: 10.56211/HELLOWORLD.V2I3.346.

M. I. Prasetya et al., “Prapemrosesan untuk Klasifikasi Gambar Aksara OKU Timur,” J. Teknol. Sist. Inf. Bisnis, vol. 7, no. 1, pp. 208–215, Feb. 2025, doi: 10.47233/JTEKSIS.V7I1.1629.

S. S. Chittibomma, R. K. Surapaneni, and A. Maruboina, “Facial Recognition System for Law Enforcement: An Integrated Approach Using Haar Cascade Classifier and LBPH Algorithm,” in Proc. Int. Conf. Adv. Power, Commun. Intell. Syst. (APCI), 2024, doi: 10.1109/APCI61480.2024.10616450.

Q. Aini, N. Lutfiani, H. Kusumah, and M. S. Zahran, “Deteksi dan pengenalan objek dengan model machine learning: Model YOLO,” CESS (J. Comput. Eng., Syst. Sci.), vol. 6, no. 2, pp. 192, 2021.

R. Kosasih and C. Daomara, “Pengenalan Wajah dengan Menggunakan Metode Local Binary Patterns Histograms (LBPH),” J. Media Inform. Budidarma, vol. 5, no. 4, p. 1258, Oct. 2021, doi: 10.30865/MIB.V5I4.3171.