Masked Face Detection and Recognition System Using HOG Algorithm


  • Maryam Sarmad M. Ali, Fattah Alizade


No Keywords


The emergence of COVID-19 pandemic at the end of 2019 has introduced new problem to the face detection and recognition systems as faces are covered with masks that in return lead to reduction in the accuracy of face identification. For this reason, new adaptation to the previously available methods are becoming a necessary work in this area. In this paper, at first we are going to create a masked dataset to be used in our work that will mainly be divided into two parts, first to propose an algorithm for mask detection using Viola Jones algorithm, and second to suggest a face recognition algorithm that would use two methods namely (using pre-trained convolutional Neural Network CNN architectures Resnet-50 and Mobilenet, and building a customized CNN). The evaluation of this method was done on the dataset that we created at the beginning of our work based on the use of FEI dataset. The mask detection algorithm used has provided and accuracy of 79% whereas the face recognition methods has shown accuracy ranging between 75% and 99%.


Download data is not yet available.


Kazemi, Vahid & Sullivan, Josephine. (2014). One Millisecond Face Alignment with an Ensemble of Regression Trees. 10.13140/2.1.1212.2243.

Viola, Paul & Jones, Michael. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE Conf Comput Vis Pattern Recognit. 1. I-511. 10.1109/CVPR.2001.990517.

A. Elmahmudi and H. Ugail, "Experiments on Deep Face Recognition Using Partial Faces," 2018 International Conference on Cyberworlds (CW), Singapore, 2018, pp. 357-362, doi: 10.1109/CW.2018.00071.

Anwar, A., & Raychowdhury, A. (2020). Masked face recognition for secure authentication. arXiv preprint arXiv:2008.11104.

Dalal, N. and Triggs, B., 2005, June. Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 1, pp. 886-893). Ieee.

Simonyan, Karen & Zisserman, Andrew. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 1409.1556.

He, Kaiming & Zhang, Xiangyu & Ren, Shaoqing & Sun, Jian. (2016). Deep Residual Learning for Image Recognition. 770-778. 10.1109/CVPR.2016.90.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2818-2826, doi: 10.1109/CVPR.2016.308.

Howard, Andrew & Zhu, Menglong & Chen, Bo & Kalenichenko, Dmitry & Wang, Weijun & Weyand, Tobias & Andreetto, Marco & Adam, Hartwig. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Kamencay, Patrik & Benco, Miroslav & Mizdos, Tomas & Radil, Roman. (2017). A New Method for Face Recognition Using Convolutional Neural Network. Advances in Electrical and Electronic Engineering. 15. 10.15598/aeee.v15i4.2389.

C. E. Thomaz and G. A. Giraldi. A new ranking method for Principal Components Analysis and its application to face image analysis, Image and Vision Computing, vol. 28, no. 6, pp. 902-913, June 2010.




How to Cite

Fattah Alizade, M. S. M. A. . (2024). Masked Face Detection and Recognition System Using HOG Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 310–315. Retrieved from



Research Article