TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Information and Optimization Sciences
Open Access Research Article

Leveraging convolutional neural networks for real-time student attendance tracking

* , , ,

* Corresponding author · click or hover a name for details

pp. 43–52Vol. 46Issue 1January 2025DOI: 10.47974/JIOS-1850XML
Received:
08 Aug 2024
Published Online:
19 Feb 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1850
Pages:
43–52

Abstract

This study presents an advanced Convolutional Neural Network (CNN) for facial detection and identification, outperforming traditional models. The method uses a channel-wise separable CNN to extract key characteristics from images, paired with Softmax classifiers and Support Vector Machine (SVM) for precise categorization. The system was applied in a smart classroom to monitor student attendance, achieving a 98.11% success rate on the labeled faces in the Wild database. The cloud-based edge computing architecture demonstrated potential to enhance security and effectiveness in educational contexts, with notable improvements in accuracy and efficiency compared to existing methods.

Keywords

FaceNetCNNDetectionLFWSVMSoftmax

Subject Classifications

68T07

References

[1] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 815–823. Accessed: Jul. 04 (2024).
[2] Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep Learning Face Attributes in the Wild,” presented at the Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3730–3738. Accessed: Jul. 04 (2024). [Online].
[3] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1701–1708. Accessed: Jul. 04 (2024). [Online].
[4] Y. Sun, X. Wang, and X. Tang, “Deep Learning Face Representation from Predicting 10,000 Classes,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1891–1898. Accessed: Jul. 04 (2024).
[5] G. P. Warman and G. P. Kusuma, “Face recognition for smart attendance system using deep learning,” Commun. Math. Biol. Neurosci., vol. 2023, no. 0, p. Article ID 19, Feb. (2023), doi: 10.28919/cmbn/7872.
[6] F. Azmi, A. Saleh, and A. Ridwan, “Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 11, no. 1, pp. 38–44, Jan. (2023), doi: 10.55524/ijircst.2023.11.1.9.
[7] A. Lateef and M. Kamil, “Face Recognition-Based Automatic Attendance System in a Smart Classroom,” Iraqi J. Electr. Electron. Eng., vol. 20, no. 1, pp. 37–47, Jun. (2024), doi: 10.37917/ijeee.20.1.4.
[8] Y. Seah, “Smart attendance system with IoT-based face recognition using deep learning approach,” other, UTAR, 2023. Accessed: Jul. 04 (2024). [Online]. Available: http://eprints.utar.edu.my/5821/
[9] A. Aggarwal, M. Alshehri, M. Kumar, P. Sharma, O. Alfarraj, and V. Deep, “Principal component analysis, hidden Markov model, and artificial neural network inspired techniques to recognize faces,” Concurr. Comput. Pract. Exp., vol. 33, no. 9, p. e6157 (2021), doi: 10.1002/cpe.6157.
[10] E. Hajri, F. Hafeez, and A. V, “Fully Automated Classroom Attendance System,” Int. J. Interact. Mob. Technol. IJIM, vol. 13, p. 95, Aug.  (2019), doi: 10.3991/ijim.v13i08.10100.
[11] A. Lohar, S. Patel, R. Mehta, P. Shah, and N. Desai, “Smart Attendance System Through Facial Recognition,” (May 2023). [Online]. Available: https://www.creativecommons.org/licenses/by/4.0/.
[12] M. Rahman, K. Rumman, R. Ahmmed, A. Rahman, and M. Sarker, “Fingerprint Based Biometric Attendance System,” Eur. Chem. Bull., vol. 12, pp. 184–190, Mar. (2023), doi: 10.31838/ecb/2023.12.s3.026.
[13] R. Alqudah, A. Arafat, M. Shawish, and H. A. Shehadeh, “Biometric-based smart attendance management system using face recognition and authentication,” AIP Conf. Proc., vol. 2979, no. 1, p. 020004, Oct. (2023), doi: 10.1063/5.0174784.
[14] N. A. M. Zulkefli, H. S. Kashoob, M. H. M. Adnan, and N. H. M. Ariffin, “Smart Attendance System Utilizing Face Recognition Technology Based on Principal Component Analysis,” in Technology: Toward Business Sustainability, B. Alareeni and A. Hamdan, Eds., Cham: Springer Nature Switzerland, pp. 214–223 (2024), doi: 10.1007/978-3-031-54019-6_20.
[15] A. Khan and I. Sharma, “Machine Learning-Based Methodology for Preventing Ransomware Attacks on Healthcare Sector,” in 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), pp. 1–5, No. (2023). doi: 10.1109/RMKMATE59243.2023.10368971.
[16] A. R. Singh, G. Singh, and N. Saluja, “Long-Short Term Memory Analysis of EEG Data Using Python for Emotion Classification,” in 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), pp. 1–5, Dec. (2023), doi: 10.1109/SMARTGENCON60755.2023.10442499.
[17] S. J. Karale, N. Mangrulkar, S. S. Badhiye, A. B. Patil, and D. Bhoyar, “Performance evaluation of machine learning algorithms for Parkinson’s disease identification model,” J. Stat. Manag. Syst., vol. 27, no. 2, pp. 237–247 (2024), doi: 10.47974/JSMS-1250.

Views: 290Downloads: 133Citations: 0