Open Access
Issue |
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|
|
---|---|---|
Article Number | 03028 | |
Number of page(s) | 7 | |
Section | Computing | |
DOI | https://doi.org/10.1051/itmconf/20224403028 | |
Published online | 05 May 2022 |
- Nyein, Thida and Aung Nway Oo. “University Classroom Attendance System Using FaceNet and Support Vector Machine.” 2019 International Conference on Advanced Information Technologies (ICAIT) (2019): 171–176. [CrossRef] [Google Scholar]
- Ketan N. Mahajan and Nagaraj V. Dharwadkar, “Classroom Attendance System Using Surveillance Camera”,Int. j. innov. eng. res. technol., pp. 1–4, Mar. 2021. [Google Scholar]
- V. Patil, A. Narayan, V. Ausekar and A. Dinesh, “Automatic Students Attendance Marking System Using Image Processing And Machine Learning,” 2020 International Conference on Smart Electronics and Communication (ICOSEC), 2020, pp. 542–546, doi: 10.1109/ICOSEC49089.2020.9215305. [CrossRef] [Google Scholar]
- A. Elmahmudi and H. Ugail, “Experiments on Deep Face Recognition Using Partial Faces,” 2018 International Conference on Cyberworlds (CW), 2018, pp. 357–362, doi: 10.1109/CW.2018.00071. [CrossRef] [Google Scholar]
- S. Dev and T. Patnaik, “Student Attendance System using Face Recognition,” 2020 International Conference on Smart Electronics and Communication (ICOSEC), 2020, pp. 90–96, doi: 10.1109/ICOSEC49089.2020.9215441. [CrossRef] [Google Scholar]
- Yohei Kawaguchi, Tetsuo Shoji, “Face Recognitionbased Lecture Attendance System”, 2005. [Google Scholar]
- Hubballi, Kirti Bhat, Vandana, “Attendance System for Face Recognition using GSM module”, International Journal of Computer Sciences and Engineering, (2018), 6. 1180–1184. doi: 10.26438/ijcse/v6i7.11801184. [CrossRef] [Google Scholar]
- S. Bhattacharya, G. S. Nainala, P. Das and A. Routray, “Smart Attendance Monitoring System (SAMS): A Face Recognition Based Attendance System for Classroom Environment,” 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), 2018, pp. 358–360, doi: 10.1109/ICALT.2018.00090. [CrossRef] [Google Scholar]
- E. Varadharajan, R. Dharani, S. Jeevitha, B. Kav-Inmathi and S. Hemalatha, “Automatic attendance management system using face detection,” 2016 Online International Conference on Green Engineering and Technologies (IC-GET), 2016, pp. 1–3, doi: 10.1109/GET.2016.7916753. [Google Scholar]
- J.-M. Guo, C.-C. Lin, M.-F. Wu, C.-H. Chang and H. Lee, “Complexity Reduced Face Detection Using Probability-Based Face Mask Prefiltering and PixelBased Hierarchical-Feature Adaboosting,” in IEEE Signal Processing Letters, vol. 18, no. 8 pp. 447–450, Aug. 2011, doi: 10.1109/LSP.2011.2146772. [CrossRef] [Google Scholar]
- S. Z. Li, R. Chu, S. Liao and L. Zhang, “Illumination Invariant Face Recognition Using Near-Infrared Images,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4 pp. 627–639, April 2007, doi: 10.1109/TPAMI.2007.1014. [CrossRef] [Google Scholar]
- Samal, Ashok and Prasana A. Iyengar. “Automatic recognition and analysis of human faces and facial expressions: a survey.” Pattern Recognition. 25 (1992): 65–77. [CrossRef] [Google Scholar]
- R. Brunelli and T. Poggio, “Face recognition: features versus templates,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 10 pp. 1042–1052, Oct. 1993, doi: 10.1109/34.254061. [CrossRef] [Google Scholar]
- Guodong Guo, S.Z. Li and Kapluk Chan, “Face recognition by support vector machines,” Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), 2000, pp. 196–201, doi: 10.1109/AFGR.2000.840634. [CrossRef] [Google Scholar]
- Puri, D., Ingle, R., Kachare, P., Awale, R., “Wavelet Packet Sub-band Based Classification of Alcoholic and Controlled State EEG Signals”, International Conference on Communication and Signal Processing (ICCASP), Atlantis Press, 2016, pp. 562–567, DOI: 10.2991/iccasp-16.2017.82. [Google Scholar]
- Kehri V., Puri D., Awale R.N., “Entropy-Based Facial Movements Recognition Using CPVM”, Applied Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1155, 2020, Springer, Singapore. https://doi.org/10.1007/97898115402952. [Google Scholar]
- Puri D., Nalbalwar S., Nandgaonkar A., “EEG Based Diagnosis of Alzheimer’s Disease Using Kolmogorov Complexity”, Applied Information Processing Systems. Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9-15. [Google Scholar]
- M. Swapna, Y. K. Sharma, and B. Prasad, “A survey on face recognition using convolutional neural network” in Data Engineering and Communication Technology. Springer, 2020, pp. 649–661. [CrossRef] [Google Scholar]
- A. El-Sawy, E.-B. Hazem, and M. Loey, “Cnn for handwritten arabic digits recognition based on lenet-5” in International conference on advanced intelligent systems and informatics. Springer, 2016, pp. 566–575. [Google Scholar]
- Z. Zhiyao and B. M. Di, “Multiplet selection: a practical study of multi- faces recognition for student attendance system” in Proceedings of the 2nd International Conference on Image and Graphics Processing, 2019, pp. 42–46. [CrossRef] [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.