Masked Face Detection and Recognition System Using HOG Algorithm
Keywords:
No KeywordsAbstract
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%.
Downloads
References
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.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
