Authors :
Mohammed Omer Farooq; Mohammed Saad; Mohammed Mueez Gous; Mir Mushtaq Ali; Mohazzeba Tanveer Raza
Volume/Issue :
Volume 11 - 2026, Issue 5 - May
Google Scholar :
https://tinyurl.com/y6vs5h32
Scribd :
https://tinyurl.com/mr37zxa5
DOI :
https://doi.org/10.38124/ijisrt/26may1752
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Automated attendance management has emerged as a critical area of research in the domains of computer vision and artificial intelligence, offering efficient, contactless, and proxy-free alternatives to conventional manual methods. This survey presents a comprehensive review of face recognitionbased automated attendance systems, covering classical machine learning approaches such as Haar Cascade, Local Binary Pattern Histograms (LBPH), and Histogram of Oriented Gradients (HOG), as well as advanced deep learning methods including Multi-task Cascaded Convolutional Networks (MTCNN), FaceNet, ResNet-50, YOLOFace, and RetinaFace. The paper systematically examines fifteen recent works, analyzing their architectures, datasets, evaluation metrics (accuracy, precision, recall, F1-score, and inference time), and deployment environments ranging from classroom settings to corporate contexts. Key challenges including variable illumination, occlusion, multi-face detection, and real-time processing are discussed. A comparative analysis reveals that deep learning-based systems, particularly those integrating MTCNN for detection and FaceNet or ResNet- 50 for recognition, consistently outperform classical methods, with accuracies exceeding 98% under normal conditions. Cloudbased solutions leveraging AWS Rekognition demonstrate 100% detection accuracy in controlled scenarios. The survey concludes by identifying open research gaps and future directions including edge deployment, liveness detection, multi-modal biometrics, and privacy-preserving attendance systems.
Keywords :
Face Detection, Face Recognition, Automated Attendance System, Haar Cascade, MTCNN, FaceNet, ResNet-50, LBPH, YOLOFace, RetinaFace, Deep Learning, Convolutional Neural Networks, Classroom Environment.
References :
- G. F. Ananda, H. A. Nugroho, and I. Ardiyanto, “Comparative Analysis of Multi-Face Detection Methods in Classroom Environments: Haar Cascade, MTCNN, YOLOFace, and RetinaFace,” in 2024 7th International Conference on Vocational Education and Electrical Engineering (ICVEE), IEEE, 2024, pp. 268–273.
- M. Kamruzzaman, M. E. A. Sheikh, M. R. Hossain, and M. M. B. Mojumdar, “Automated Attendance System for the Entire Classroom with Multi-Face Facial Recognition via AWS Rekognition,” in 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE, 2025.
- L. Li and Q. Zhang, “Intelligent Education Management System Design for Universities Based on MTCNN Face Recognition Algorithm,” Scalable Computing: Practice and Experience, vol. 25, no. 4, pp. 2463– 2475, 2024.
- S. P. Krishnan and A. Manikuttan, “Attendance Management System Using Facial Recognition,” in 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), IEEE, 2022.
- O. Baker, K. Subaramaniam, D. Lopez, S. Palaniappan, and W. Li, “A Deep Learning-Based Approach Using MTCNN and FaceNet for Automated Attendance and Security Monitoring Systems,” in 2025 IEEE 7th Symposium on Computers & Informatics (ISCI), IEEE, 2025, pp. 104–109.
- A. Ghodekar, A. Phapale, T. Gunjal, D. Thorat, A. Landage, and R. Nikam, “A Face Recognition-Based Smart Student Attendance and Activeness Monitoring System,” in 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), IEEE, 2023.
- N. D. R, S. M, A. R. S, P. M. Amirthavarshini, A. Vinora, and K. Swathi, “An Efficient Face Recognition Based Attendance System,” in 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), IEEE, 2023, pp. 155–160.
- A. Yadav, A. Sharma, and S. S. Yadav, “Attendance Management System Based on Face Recognition Using Haar-Cascade,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, 2022, pp. 1972–1976.
- I. Rajput, S. Srivastava, O. Dahiya, N. Nazir, A. Sarwar, S. Aggarwal, N Kaur, and B. Kaur, “Attendance Management System using Facial Recognition,” in 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), IEEE, 2022, pp. 797–801.
- N. Kumar, Sheetal, V. K. N, and Alli, “Smart Attendance by Using Face Recognition,” in 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), IEEE, 2025.
- S. Rathi, S. Kapadnis, G. Sonar, G. Sonar, and S. R. Vispute, “MultiFacial Automated Attendance System using Haar Cascade, LBPH, and OpenCV-Based Face Detection and Recognition,” in 2023 7th International Conference on Computing, Communication, Control and Automation (ICCUBEA), IEEE, 2023.
- M. Angulakshmi and V. Susithra, “Face Recognition Based Attendance Management System Using Deep Learning Method,” in 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), IEEE, 2024.
- G. V. V. Reddy, Y. Manvitha, G. Ravi Teja, G. Maresh, and A. De, “Development of Attendance Monitoring System using Facial Recognition,” in 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), IEEE, 2025.
- G. R. Venkatakrishnan, R. Rengaraj, R. Jeya, S. K. R, S. Manikandan, N Divya Dharshini, and M. A. Abishek, “Design and Implementation of Automated Attendance System Using Contactless Facial Recognition,” in 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), IEEE, 2024.
- L. S, K. P. Karthik, T. S. Gupta, A. M. V, and A. J. Sreekar, “AIDriven Attendance Tracking with Haar Cascade and ResNet,” Procedia Computer Science, vol. 258, pp. 3544–3553, 2025.
- P. Viola and M. J. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, May 2004.
Automated attendance management has emerged as a critical area of research in the domains of computer vision and artificial intelligence, offering efficient, contactless, and proxy-free alternatives to conventional manual methods. This survey presents a comprehensive review of face recognitionbased automated attendance systems, covering classical machine learning approaches such as Haar Cascade, Local Binary Pattern Histograms (LBPH), and Histogram of Oriented Gradients (HOG), as well as advanced deep learning methods including Multi-task Cascaded Convolutional Networks (MTCNN), FaceNet, ResNet-50, YOLOFace, and RetinaFace. The paper systematically examines fifteen recent works, analyzing their architectures, datasets, evaluation metrics (accuracy, precision, recall, F1-score, and inference time), and deployment environments ranging from classroom settings to corporate contexts. Key challenges including variable illumination, occlusion, multi-face detection, and real-time processing are discussed. A comparative analysis reveals that deep learning-based systems, particularly those integrating MTCNN for detection and FaceNet or ResNet- 50 for recognition, consistently outperform classical methods, with accuracies exceeding 98% under normal conditions. Cloudbased solutions leveraging AWS Rekognition demonstrate 100% detection accuracy in controlled scenarios. The survey concludes by identifying open research gaps and future directions including edge deployment, liveness detection, multi-modal biometrics, and privacy-preserving attendance systems.
Keywords :
Face Detection, Face Recognition, Automated Attendance System, Haar Cascade, MTCNN, FaceNet, ResNet-50, LBPH, YOLOFace, RetinaFace, Deep Learning, Convolutional Neural Networks, Classroom Environment.
