Abstract
An attendance system has always been a critical instrument that is normally engaged to determine the response of persons to events, programs, or scheduled classes within the educational context. The Student Industrial Work Experience Scheme (SIWES) is a work-integrated learning program developed and made mandatory for all undergraduate students in professional and sciences courses in the Nigerian Higher Education Institutions (HEIs). This is to expose the students to the world of works in the industry before graduation. However, monitoring students and ensuring that they partake fully in the scheme has proven difficult over the years. In this paper, we developed a Convolutional Neural Network (CNN) model named DeepFacematch to realize face recognition based contactless attendance. Given the model’s validation accuracy of 92.60%, a contactless attendance app containing the validated model and location tracking support was developed, for incorporation into the e-SIWES web portal. This is to achieve effective monitoring of the students’ compliance with the rules of the work experience scheme. Remarkably, the addition of the DeepFacematch feature will be a boost to the viability of e-SIWES portal for improved SIWES coordination across the HEIs in Nigeria. DeepFacematch can also be re-trained and adapted for contactless attendance systems at offices and schools to curb the spread of infectious diseases like coronavirus.
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References
Adetiba, E., Matthews, V., Egunjobi, V., Olajide, A.: Development of e-SIWES portal: a web based platform for student industrial work experience scheme (SIWES) management. Int. J. Bus. Inf. Syst. 3, 10–17 (2012)
Adetiba, E., Iortim, O., Olajide, A.T., Awoseyin, R.: OBCAMS: an online biometrics-based class attendance management system. Afr. J. Comput. ICT 6(3), 25–38 (2013)
Lodha, R., Gupta, S., Jain, H., Narula, H.: Bluetooth smart based attendance management system. Procedia Comput. Sci. 45, 524–527 (2015)
Pireva, K.R., Siqeca, J., Berisha, S.: RFID: management system for students’ attendance. IFAC Proc. Volumes 46, 137–140 (2013)
Nagothu, S.K., Kumar, O.P., Anitha, G.: GPS Aided Auton. Monit. Attendance Syst. Procedia Comput. Sci. 87, 99–104 (2016)
Sunaryono, D., Siswantoro, J., Anggoro, R.: An android based course attendance system using face recognition. J. King Saud Univ. Comput. Inf. Sci, 1–9 (2019)
Ahmed, A., Olaniyi, O.M., Kolo, J.G., Durugo, C.: A multifactor student attendance management system using fingerprint biometrics and RFID techniques. In: International Conference on Information and Communication Technology and Its Applications (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Yousaf, M.H., Baloch, N.K., Ahmad, W., Baig, M.: Algorithm efficient attendance management face recognition based approach. Int. J. Comput. Sci. Issues 9(4), 146 (2012)
P. Viola and M. J. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, vol. 57, pp. 137–154, 2004/05/01 2004.
Albakri, A., Mokbel, C.: Convolutional neural network biometric cryptosystem for the protection of the blockchain’s private key. Procedia Comput. Sci. 160, 235–240 (2019)
Cheon, M.-K., Lee, W.-J., Hyun, C.-H., Park, M.: Rotation invariant histogram of oriented gradients. Int. J. Fuzzy Logic Intell. Syst. 11(4), 293298 (2011)
Li, X.K., Zhang, G., Zheng, W.: Deep learning and its parallelization. Big Data (12, 2016)
Fang, Z., Feng, H., Huang, S., Zhou, D.X.: Theory of deep convolutional neural networks II: spherical analysis. Neural Netw. 131, 154–162 (2020)
Abadi, M., et al.: Tensorflow: A system for large-scale machine learning. In: 12th {USENIX} symposium on operating systems design and implementation, {OSDI} 2016, pp. 265–283 (2016)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Borovikov, E., Vajda, S.: FaceMatch: real-world face image retrieval. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 405–419. Springer, Singapore (2017)
Mohammed, K., Tolba, A.S., Elmogy, M.: Multimodal student attendance management system (MSAMS). Ain Shams Eng. J. 9, 2917–2929 (2018)
Acknowledgments
This work was carried out at the Advanced Signal Processing and Machine Intelligence Research (ASPMIR) Group laboratory, IoT Enabled Smart and Connected Community Research Cluster, Covenant University. The High Performance Computing node of the Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE) FEDGEN Testbed was utilized for experimentations. The Covenant University Centre for Research, Innovation and Development (CUCRID), Covenant University, Ota, Nigeria provided full sponsorship for the publication of this work.
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Adetiba, E., Opara, A.E., Ajayi, O.T., Owolabi, F.O. (2021). DeepFacematch: A Convolutional Neural Network Model for Contactless Attendance on e-SIWES Portal. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_16
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