Student Attendance System Using Face Detection Features Based on Cognitive Internet of Things
Article Sidebar
Read Counter : 612
Download : 544
Main Article Content
Abstract
Attendance fraud is often found in the presence of fake attendance from students. To overcome this problem, we apply the Haar-Like Feature Cascade method as the basis for building a face attendance system based on the Cognitive Internet of Things (CIoT). The proposed system works by representing local intensity patterns on the face image so that it will recognize students even from various frontal positions. The results of this research show a precise student face recognition that is able to recognize faces well. The conclusion of this research is that the Haar-like Feature Cascade method is able to detect faces precisely and can be used as a basis for developing student attendance technology based on the Cognitive Internet of Things.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
References
Amylia AR, A., April Liana, D., Maharani, A., Kristianto, B., Ahmad, & Ilham, A. (2023). Sample Face Q.Dsent Database (SFQ2D). Zenodo. https://doi.org/10.5281/zenodo.8378429
Bayaumi, S., Aldayel, A., Alotaibi, M., Aldraihem, M., Alrashed, S., & Alzahrahi, S. (2015). Class attendance system based-on palm vein as biometric information. Journal of Theoretical & Applied Information Technology, 77.
Islam, M. M., Hasan, M. K., Billah, M. M., & Uddin, M. M. (2017). Development of smartphone-based student attendance system. 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 230–233. https://doi.org/10.1109/R10-HTC.2017.8288945
Kim, M., Xin, Q., Sinha, S., & Orso, A. (2022). Automated test generation for REST APIs: no time to rest yet. Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, 289–301. https://doi.org/10.1145/3533767.3534401
Mehta, P., & Tomar, P. (2016). An efficient attendance management sytem based on face recognition using Matlab and Raspberry Pi 2. International Journal of Engineering Technology Science and Research IJETSR, 3, 71–78.
Mohamed, B. K. P., & Raghu, C. V. (2012). Fingerprint attendance system for classroom needs. 2012 Annual IEEE India Conference (INDICON), 433–438. https://doi.org/10.1109/INDCON.2012.6420657
Mohandes, M. A. (2017). Class Attendance Management System Using NFC Mobile Devices. Intelligent Automation & Soft Computing, 23(2), 251–259. https://doi.org/10.1080/10798587.2016.1204749
Noor, S. A. M., Zaini, N., Latip, M. F. A., & Hamzah, N. (2015). Android-based attendance management system. 2015 IEEE Conference on Systems, Process and Control (ICSPC), 118–122. https://doi.org/10.1109/SPC.2015.7473570
Pittle, P. (2012). Automated Web Service Inventory Management Software. Grid Computing.
Rahni, A. A. A., Zainal, N., Zainal Adna, M. F., Othman, N. E., & Bukhori, M. F. (2015). Development of the online student attendance monitoring system (SAMSTM) based on QR-codes and mobile devices. Journal of Engineering Science and Technology, 10(Spec. Issue 2 on UKM Teaching and Learning Congress 2013, May 2015), 28–40.
Rjeib, H. D., Ali, N. S., Al Farawn, A., Al-Sadawi, B., & Alsharqi, H. (2018). Attendance and information system using RFID and web-based application for academic sector. International Journal of Advanced Computer Science and Applications, 9(1), 266–274. https://doi.org/10.14569/IJACSA.2018.090137
Yusof, Y. W. M., Nasir, M. A. M., Othman, K. A., Suliman, S. I., Shahbudin, S., & Mohamad, R. (2018). Real-time internet based attendance using face recognition system. International Journal of Engineering and Technology(UAE), 7(3), 174–178. https://doi.org/10.14419/ijet.v7i3.15.17524
Zainal, N. I., Sidek, K. A., & Gunawan, T. S. (2016). Portable anti forgery recognition for attendance system using fingerprint based biometric. ARPN Journal of Engineering and Applied Sciences, 11(1), 396–403.