Browse > Article
http://dx.doi.org/10.3837/tiis.2022.02.008

Web-based University Classroom Attendance System Based on Deep Learning Face Recognition  

Ismail, Nor Azman (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Chai, Cheah Wen (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Samma, Hussein (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Salam, Md Sah (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Hasan, Layla (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Wahab, Nur Haliza Abdul (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Mohamed, Farhan (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Leng, Wong Yee (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Rohani, Mohd Foad (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.2, 2022 , pp. 503-523 More about this Journal
Abstract
Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.
Keywords
Deep Learning; Pre-trained Model; Registration of Face Datasets; Face Recognition; Web-based Attendance System; Feature Vectors;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Charity, A., Okokpujie, K., & Etinosa, N. O., "A bimodal biometric student attendance system," in Proc. of 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), pp. 464-471, 2017.
2 Amri, U. F., Hashim, N. N. W. N., & Hanif, N. H. H. M., "Speech-based class attendance," in Proc. of IOP Conference Series: Materials Science and Engineering, vol. 260, no. 1, p. 012008, 2017.
3 He, K., Zhang, X., Ren, S., & Sun, J., "Deep residual learning for image recognition," in Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
4 Zhu, Y., & Jiang, Y., "Optimisation of face recognition algorithm based on deep learning multi-feature fusion driven by big data," Image and Vision Computing, vol. 104, pp. 1-8, 2020.
5 Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C., "SSD: Single shot multibox detector," in Proc. of European Conference on Computer Vision, pp. 21-37, 2016.
6 justadudewhohacks/face-api.js [Online]. Available: https://github.com/justadudewhohacks/faceapi.js/, Accessed on June 22, 2021
7 Ayop, Z., Lin, C. Y., Anawar, S., Hamid, E., & Azhar, M. S., "Location-aware event attendance system using QR code and GPS technology," International Journal of Advanced Computer Science and Applications, vol. 9, no. 9, pp. 466-473, 2018.
8 Shah, S. N., & Abuzneid, A., "IoT based smart attendance system (SAS) using RFID," in Proc. of 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp. 1-6, 2019.
9 Apoorv, R., & Mathur, P., "Smart attendance management using bluetooth low energy and android," in Proc. of 2016 IEEE Region 10 Conference (TENCON), pp. 1048-1052, 2016.
10 Schroff, F., Kalenichenko, D., & Philbin, J., "Facenet: A unified embedding for face recognition and clustering," in Proc. of The IEEE Conference On Computer Vision and Pattern Recognition, pp. 815-823, 2015.
11 Wati, V., Kusrini, K., Al Fatta, H., & Kapoor, N., "Security of facial biometric authentication for attendance system," Multimedia Tools and Applications, vol. 80, pp. 23625-23646, 2021.   DOI
12 Kak, S. F., Mustafa, F. M., & Valente, P., "A review of person recognition based on face model," Eurasian Journal of Science & Engineering, vol. 4, no. 1, pp. 157-168, 2018.
13 Sinha, P., Balas, B., Ostrovsky, Y., & Russell, R., "Face recognition by humans: Nineteen results all computer vision researchers should know about," in Proc. of the IEEE, vol. 94, no. 11, pp. 1948-1962, 2006.   DOI
14 Wang, C., Wang, Y., Chen, Y., Liu, H., & Liu, J., "User authentication on mobile devices: Approaches, threats and trends," Computer Networks, vol. 170, 2020.
15 Gardezi, S. J. S., Faye, I., Adjed, F., Kamel, N., & Hussain, M., "Mammogram classification using chi-square distribution on local binary pattern features," Journal of Medical Imaging and Health Informatics, vol. 7, no. 1, pp. 30-34, 2017.   DOI
16 Wiskott, L., Kruger, N., Kuiger, N., & Von Der Malsburg, C., "Face recognition by elastic bunch graph matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, 1997.   DOI
17 Ahmed, S. B., Ali, S. F., Ahmad, J., Adnan, M., & Fraz, M. M., "On the frontiers of pose invariant face recognition: a review," Artificial Intelligence Review, vol. 53, pp. 2571-2634, 2020.   DOI
18 Yang, J., Zhang, D., Frangi, A. F., & Yang, J. Y., "Two-dimensional PCA: a new approach to appearance-based face representation and recognition," IEEE Transactions nn Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, 2004.   DOI
19 Ahmed, S., Frikha, M., Hussein, T. D. H., & Rahebi, J., "Optimum feature selection with particle swarm optimization to face recognition system using gabor wavelet transform and deep learning," BioMed Research International, 2021.
20 Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L., "Deepface: closing the gap to human-level performance in face verification," in Proc. of the IEEE conference on computer vision and pattern recognition, pp. 1701-1708, 2014.