Published on: April 2026
FACE RECOGNITION FOR ATTENDANCE SYSTEM USING MULTI-TASK CASCADED CONVOLUTIONAL NETWORKS
ANNJOE J PRASAD
M AJIN
Article Status
Available Documents
Abstract
The widespread adoption of biometric technologies has opened new avenues for automating traditionally manual processes, with facial recognition standing out as a reliable and non-intrusive approach. This work presents an intelligent attendance management framework that employs Multi-Task Cascaded Convolutional Networks (MTCNN) as the foundational algorithm for face detection and alignment. MTCNN’s three-stage cascade—comprising the Proposal Network (P-Net), Refinement Network (R-Net), and Output Network (O-Net)—enables progressive, high-precision localization of facial regions. Recognized individuals are subsequently verified through a Convolutional Neural Network (CNN) trained with transfer learning on domain-specific data, substantially improving identification accuracy. The system captures attendance entries in real time and stores timestamped records in a structured digital format, accessible through an intuitive administrative interface. This approach eliminates the inefficiencies associated with conventional roll-call methods while providing a scalable solution for institutional settings.
How to Cite this Paper
PRASAD, A. J. (2026). Face Recognition for Attendance System using Multi-Task Cascaded Convolutional Networks. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.418
PRASAD, ANNJOE. "Face Recognition for Attendance System using Multi-Task Cascaded Convolutional Networks." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.418.
PRASAD, ANNJOE. "Face Recognition for Attendance System using Multi-Task Cascaded Convolutional Networks." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.418.
Search & Index
References
- Zhang, J. Luo, and W. Gao, “Research on face detection technology based on MTCNN,” in Proc. 2020 Int. Conf. Computer Network, Electronic and Automation (ICCNEA), 2020, pp. 154–158.
- Gu, X. Liu, and J. Feng, “Classroom face detection algorithm based on improved MTCNN,” Signal, Image Video Process., vol. 16, no. 5, pp. 1355–1362, 2022.
- Kaziakhmedov et al., “Real-world attack on MTCNN face detection system,” in Proc. 2019 Int. Multi-Conf. Engineering, Computer and Information Sciences (SIBIRCON), 2019, pp. 0422–0427.
- Wu and Y. Zhang, “MTCNN and FACENET based access control system for face detection and recognition,” Autom. Control Comput. Sci., vol. 55, pp. 102–112, 2021.
- Ghofrani, R. M. Toroghi, and S. Ghanbari, “Realtime face-detection and emotion recognition using MTCNN and MiniShuffleNet V2,” in Proc. 2019 5th Conf. Knowledge-Based Engineering and Innovation (KBEI), 2019, pp. 817–821.
- Zumstein, Python for Excel. Sebastopol, CA: O’Reilly Media, 2021.
- Ku and W. Dong, “Face recognition based on MTCNN and convolutional neural network,” Frontiers Signal Process., vol. 4, no. 1, pp. 37–42, 2020.
- Chen et al., “Eyes localization algorithm based on prior MTCNN face detection,” in Proc. 2019 IEEE 8th Joint Int. ITAIC
- Conf., 2019, pp. 1763–1767.
- Fu, M. Kim, and J. W. Jang, “Research and optimization of face detection algorithm based on MTCNN in a complex environment,” J. Korea Inst. Inf. Commun. Eng., vol. 24, no. 1, pp. 50–56, 2020.
- Ma and J. Wang, “Multi-view face detection and landmark localization based on MTCNN,” in Proc. 2018 Chinese Automation Congress (CAC), 2018, pp. 4200–4205.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 17 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.