Open Access
| Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/epjconf/202532801002 | |
| Published online | 18 June 2025 | |
- Xing, F.F., You, Z., 2023. Research on Face Image Recognition System Based on Computer Artificial Intelligence Technology. Presented at the 2023 IEEE 3rd International Conference on Electronic Technology, Communication, and Information (ICETCI). IEEE, [Google Scholar]
- Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks. IEEE Signal Processing Letters, 3(10), 1499–1503. DOI: 10.1109/LSP.2016.2603342 [CrossRef] [Google Scholar]
- Gumus, E., Kilic, N., Sertbas, A., & Ucan, O.N. (2010). Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Systems with Applications, 37(9),6404–6408 [CrossRef] [Google Scholar]
- He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1904–1916 [CrossRef] [Google Scholar]
- Alhanaeea, K., Alhammadia, M., Almenhalia, N., Shatnawia, M., 2021. Face Recognition Smart Attendance System Using Deep Transfer Learning. Procedia Computer Science,184, pp. 789–796. [Google Scholar]
- Hashmi, S.A., 2022. Face Detection in Extreme Conditions: A Machine-learning Approach. Journal of Applied Machine Learning, 15(2), pp. 112–120. [Google Scholar]
- Hasan, M., Rahman, R.M.M.U., Taief, A.M.O.H.A.M.M.A.D., 2022. Improvement of Face and Eye Detection Performance by Using Multi-task Cascaded Convolutional Networks. Computer Vision and Image Understanding, 134(3), pp. 56–64. [Google Scholar]
- Hallaji E, Razavi-Far R, Saif, M. DLIN: Deep Ladder Imputation Network. IEEE TransCybern. 2022 Sep;52(9):8629–8641. doi:10.1109/TCYB.2021.3054878. Epub 2022 Aug PMID: 33661751. [Google Scholar]
- Arjun, E.L., et al. "Advanced Face Authentication Using Deep Learning Models." 2023 IEEE Pune Section International Conference (PuneCon). IEEE, 2023. [Google Scholar]
- Bileschi S, Heisele B (2002) Advances in Component Based Face Detection. In:Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines. Springer-Verlag, London, UK [Google Scholar]
- Li, C., Li, R., Sun, J. : CNN face live detection algorithm based on binocular camera. J.Phys. Conf. Ser. 1881(2): 022015 (7pp) (2021) [CrossRef] [Google Scholar]
- Anitha, G., Devi, P.S., Sri, J.V., & Priyanka, D. (2020). Face Recognition Based Attendance System Using MTCNN and FaceNet. Zeichen Journal, 6(1), 189–195. [Google Scholar]
- Gornale, B., & Kiran, P. (2020). Classroom Attendance Management System Using Camera. International Journal of Research in Engineering, Science and Management, 3(8), 327–330. [Google Scholar]
- Khan, M.Z., Harous, S., Hassan, S.U., Khan, M.U.G., Iqbal, R., & Mumtaz, S. (2019). Deep Unified Model for Face Recognition Based on Convolution Neural Network and Edge Computing. IEEE Access, 7,72622–72633 [CrossRef] [Google Scholar]
- Goyal, A., Dalvi, A., Guin, A., Gite, A., & Thengade, A. (2021). Online Attendance Management System Based on Face Recognition Using CNN. In 2nd International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS 2021). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.