Masked Face Recognition Attendance System Using A Modified Convolutional Neural Network

Main Article Content

Jun Jie How
Shing Chiang Tan
Kim Soon Liew


In this paper, a masked face recognition based attendance system is developed by modifying a version of convolutional neural network (CNN). In this regard, a Support Vector Machine is integrated in the CNN to replace its original Softmax classifier to perform the task. The performance of the modified CNN in recognizing masked faces in a 5-fold cross validation was compared that of other CNNs. The experimental results show high effectiveness of the proposed CNN (i.e.  98.92%) in recognizing masked faces for recording attendance.

Article Details



A. Fong, “Fingerprint: Card access control and time attendance solutions : Fingertec Worldwide,” Fingerprint | Card Access Control And Time Attendance Solutions : FingerTec Worldwide, Jun-2005. [Online]. Available:

C. B. Chew, M. Mahinderiit-Singh, K. C. Wei, T. W. Sheng, M. H. Husin, N. Hashimah, and A. H. Malim, “Sensors-enabled smart attendance systems using NFC and RFID Technologies,” International Journal of New Computer Architectures and their Applications, vol. 5, no. 1, pp. 19–28, 2015.

N. Kar, M. K. Debbarma, A. Saha, and D. R. Pal, “Study of implementing automated attendance system using face ... - IJCCE,” International Journal of Computer and Communication Engineering, vol. 1, no.2, pp. 100-103, 2012.

S.-H. Lin. (2000). “An Introduction to face recognition technology,” Informing Science: The International Journal of an Emerging Transdicipline, vol. 3, no 1, pp. 001 – 007, 2000.

V. Agarwal, “Face detection models: Which to use and why?,” Medium, 02-Jul-2020. [Online]. Available:

L. F. Lung, M. N. Barua, and P. S. Juarez, “An image acquisition method for face recognition and implementation of an automatic attendance system for events,” in 2019 IEEE XXVI International Conference on Electronics, Electrical Engineering and Computing (INTERCON), 2019, pp. 1–4.

S. Nasr, K. Bouallegue, M. Shoaib, and H. Mekki, “Face recognition system using bag of features and multi-class svm for robot applications,” in 2017 International Conference on Control, Automation and Diagnosis (ICCAD), 2017, pp. 263-268.

V. Chawda, V. Arya, S. Pandey, Shristi, and M. Valleti, “Unique Face Identification System using machine learning,” 2020 Second International Conference on Inventive Research in Computing Applications (ICIRICA), 2020, pp. 701-706.

H. S. Karthink and J. Manikandan, “Evaluation of relevance vector machine classifier for a real-time face recognition system,” in 2017 IEEE International Coference on Consumer Electronics-Asia (ICCE-Asia), 2017, pp. 26-30.

E. Winarno, I. H. A. I. Amin, H. Februariyanti, P. W. Adi, W. Hadikurniawati, and M. T. Anwar, “Attendance system based on face recognition system using cnn-pca method and real-time camera,” in 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2019, pp. 301-304.

X. Qu, T. Wei, C. Peng, and P. Du, “A fast face recognition system based on deep learning,” in 2018 11th International Symposium on Computational Intelligence and Design (ISCID), 2018, pp. 289-292.

M. Arsenovic, S. Sladojevic, A. Anderia, and D. Stefanovic, “Facetime - deep learning based face recognition attendance system,” in 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), 2017, pp. 000053-000058.

T. Lv, C. Wen, J. Zhang, and Y. Chen, “A face recognition algorithm based on cnn with elbp and dcgan,” in 2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), 2020, pp. 99-102.

S. Chen, W. Liu, and G. Zhang, “Efficient transfer learning combined skip-connected structure for masked face poses classification,” IEEE Access, vol. 8, pp. 209688-209698, 2020.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556, 2014. [Online]. Available:

S. Mark, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” arXiv preprint arXiv: 1801.04381v4, 2019. [Online]. Available: