Development of a Real-Time Attendance System Based on Face Recognition Using the Viola-Jones Algorithm
Keywords:
Face Recognition, Viola-Jones, Haar Cascade, Attendance, real timeAbstract
The development of computer vision technology is driving the transformation of attendance systems from conventional methods to biometric-based automated systems. Manual attendance systems still have weaknesses such as the potential for fraud, low efficiency, and difficulties in data management. This study aims to develop a face recognition-based attendance system that can work in real time using the Viola-Jones algorithm. The methods used include image conversion to grayscale, feature extraction using Haar Features, computational acceleration through integral image, and classification processes using AdaBoost and Cascade Classifier. The system is implemented using Python and the OpenCV library with a camera as the main input. Testing was carried out based on several parameters, namely face distance, lighting conditions, and face position relative to the camera. The results show that the system is able to detect and recognize faces in real time with a good level of accuracy under optimal lighting conditions and face positions, although it experiences a decrease in performance under low lighting conditions and extreme face angles. The conclusion of this study is that the Viola-Jones algorithm is effective for use in face recognition-based attendance systems with low computational requirements. The contribution of this research lies in the development of an attendance system that is efficient, practical, and able to minimize the potential for fraud compared to conventional methods.
References
[1] B.-T. Nguyen-Tat, M.-Q. Bui, and V. M. Ngo, “Automating attendance management in human resources: A design science approach using computer vision and facial recognition,” Int. J. Inf. Manag. Data Insights, vol. 4, no. 2, p. 100253, 2024, doi: https://doi.org/10.1016/j.jjimei.2024.100253.
[2] W. M. Alshamsi et al., “Computer Vision-Based Attendance System - A Review,” in 2024 Arab ICT Conference (AICTC), 2024, pp. 11–17. doi: 10.1109/AICTC58357.2024.10735014.
[3] H. D. Aparece, J. A. A. Gambe, J. M. M. Penton, and D. B. Valdez, “Design and Development of Integrated Human Resource Management System with Face Recognition Attendance,” in 2025 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), 2025, pp. 1–6. doi: 10.1109/eStream66938.2025.11016878.
[4] A. K. Shukla, A. Shukla, and R. Singh, “Automatic Attendance System Based on CNN–LSTM and Face Recognition,” Int. J. Inf. Technol., vol. 16, pp. 1293–1301, 2024, doi: 10.1007/s41870-023-01495-1.
[5] A. A. Shabaneh, S. Qaddomi, M. Hamdan, A. Abu Sneineh, and T. Punithavathi, “Automatic Class Attendance System Using Biometric Facial Recognition Technique Based on Raspberry Pi,” Óptica Pura y Apl., vol. 56, no. 3, doi: 10.7149/OPA.56.3.51152.
[6] C. U. Betrand, C. J. Onyema, M. E. Benson-Emenike, and D. A. Kelechi, “Authentication System Using Biometric Data for Face Recognition,” Int. J. Sustain. Dev. Res., vol. 9, no. 4, pp. 68–78, Nov. 2023, doi: 10.11648/j.ijsdr.20230904.12.
[7] G. R. Venkatakrishnan et al., “Design and Implementation of Automated Attendance System Using Contactless Facial Recognition,” in 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 2024, pp. 1–6. doi: 10.1109/ICPECTS62210.2024.10780113.
[8] H. Tabassum, S. S. Hossain, S. Kumari, J. J., S. R. Sahoo, and S. Rizvi, “Facial Recognition-Based Automated Attendance System,” in 2025 3rd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), 2025, pp. 1273–1278. doi: 10.1109/ICAICCIT68829.2025.11434431.
[9] A. Umer, M. Iman, R. Sardar, W. Ahmed, and S. Saeed, “Deep Learning-Powered Attendance Tracking: A Contactless and Efficient Logging System,” Zenodo, 2025, doi: 10.5281/zenodo.15879268.
[10] Y. Jing, X. Lu, and S. Gao, “3D Face Recognition: A Comprehensive Survey in 2022,” Comput. Vis. Media, vol. 9, pp. 657–685, 2023, doi: 10.1007/s41095-022-0317-1.
[11] V. Tomar, N. Kumar, and A. R. Srivastava, “Single Sample Face Recognition Using Deep Learning: A Survey,” Artif. Intell. Rev., vol. 56, no. Suppl 1, pp. 1063–1111, 2023, doi: 10.1007/s10462-023-10551-y.
[12] S. Tariyal, R. Chauhan, Y. Bijalwan, R. Rawat, and R. Gupta, “A comparitive study of MTCNN, Viola-Jones, SSD and YOLO face detection algorithms,” in 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), 2024, pp. 1–7. doi: 10.1109/IITCEE59897.2024.10467445.
[13] M. Gu, X. Liu, and J. Feng, “Classroom Face Detection Algorithm Based on Improved MTCNN,” Signal, Image Video Process., vol. 16, pp. 1355–1362, 2022, doi: 10.1007/s11760-021-02087-x.
[14] D. M. Abdulhussien and L. J. Saud, “An Evaluation Study of Face Detection by Viola-Jones Algorithm,” Int. J. Health Sci. (Qassim)., vol. 6, no. S8, pp. 4174–4182, 2022, doi: 10.53730/ijhs.v6nS8.13127.
[15] A. Ahmed, M. Arafa, R. A. El Abd, and Z. Ahmed, “The Viola-Jones Face Detection Algorithm Analysis: A Survey,” J. Cybersecurity Inf. Manag., vol. 6, no. 2, pp. 85–95, 2021, doi: 10.54216/JCIM.060201.
[16] H. W. Min and A. S. Ab Ghafar, “Real-Time Face Detection Attendance Management System,” J. Adv. Ind. Technol. Appl., vol. 3, no. 1, pp. 32–38, 2022, doi: 10.30880/jaita.2022.03.01.005.
[17] S. G., H. Sridhar, T. S. S., M. K. S., and N. Saritakumar, “Real-Time Student Attendance System Using Face Recognition and Cloud Integration,” in 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES), 2025, pp. 1–7. doi: 10.1109/ICCIES63851.2025.11032326.
[18] K. Painuly, Y. Bisht, H. Vaidya, A. Kapruwan, and R. Gupta, “Efficient Real-Time Face Recognition-Based Attendance System with Deep Learning Algorithms,” in 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), 2024, pp. 1–5. doi: 10.1109/IITCEE59897.2024.10467743.
[19] M. B. Savadatti, A. S. Bale, N. Ghorpade, S. K. A, B. S., and R. H., “Face Recognition of Live-Feed Imagery Using the Viola-Jones Algorithm,” in 2023 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS), 2023, pp. 1–6. doi: 10.1109/CCPIS59145.2023.10291969.
[20] Sumanto, B. Wijonarko, M. Qommarudin, A. Sudibyo, P. Widodo, and A. M. Lukman, “Viola-Jones Algorithm for Face Detection using Wider Face Dataset,” in 2022 10th International Conference on Cyber and IT Service Management (CITSM), 2022, pp. 1–4. doi: 10.1109/CITSM56380.2022.9935830.
[21] B. A. Hassan and F. A. A. Dawood, “Facial image detection based on the Viola-Jones algorithm for gender recognition,” Int. J. Nonlinear Anal. Appl., vol. 14, no. 1, pp. 1593–1599, 2023, doi: 10.22075/ijnaa.2022.7130.
[22] R. R. Al-Khalidy and M. Jabardi, “Viola-Jones and the speeded-up robust transformation model for multiple face detection and recognition in the images,” in The 3rd International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022), 2022, pp. 174–183. doi: 10.1049/icp.2022.2442.
[23] M. S. Nidom, “Haar Cascade Classifier and Adaboost Algorithm for Face Detection with the Viola-Jones Method,” Trans. Informatics Data Sci., vol. 2, no. 1, pp. 15–26, 2025, doi: 10.24090/tids.v2i1.12276.
[24] T. V Sandiva, L. Yemi, and A. Ramadhanu, “Identifikasi Pengolahan Citra Pada Face Detection Menggunakan Metode Median Filtering dan Viola-Jones,” Indones. J. Comput. Sci., vol. 13, no. 2, 2024, doi: 10.33022/ijcs.v13i2.3675.
[25] A. A. M. Suradi, I. Djafar, S. Alam, and A. S. A. Syam, “Perbandingan Metode Haar Cascade dan Dlib Dalam Mendeteksi Wajah Secara Realtime,” 2023. doi: 10.36774/sisiti.v12i2.1331.
[26] M. Bahit, N. P. Utami, H. K. Candra, Y. Supit, and A. A. Ramadhan, “Validation of the Haar Cascade Classification Method in Face Detection,” J. Informatics Telecommun. Eng., vol. 7, no. 1, pp. 233–243, 2023, doi: 10.31289/jite.v7i1.10040.
