Masked Face Recognition Using Convolutional Neural Networks
DOI:
https://doi.org/10.31642/JoKMC/2018/100111Keywords:
COVID-19, CNN, Face Recognition, Masked FaceAbstract
Since the COVID-19 epidemic's rise in 2020, Cover face recognize achieve advanced significantly in the
range of computer vision. Face cover is important to stop or limit the COVID-19 disease's spread due to the global
outbreak. Face recognize is among of the most commonly used biometric recognition approach, because it can be
utilized for monitoring systems, identity management, security verifying, and a lot of applications. The majority features
of faces were hidden by mask, leaving just a quite some, including eyes plus head-region, that’s utilized for recognize.
This challenge may reduce the recognition percentage because of the limited area to extract features. Due to the
popularity of deep learning to extract and recognize deep features in many research areas especially computer vision,In
this work, a covered face recognize system is introduced. utilizing Convolutional neural network (CNN), one of the most
widely common deep learning algorithms. The final layer in the CNN architecture, the softmax activation function, was
utilized to identify the facial characteristics after they had been extracted using CNN from the masked face's eyes,
forehead, and brow regions. In the Study employ the "Extended Yale B database," which has issues with changes in
placement and lighting. additionally, they covered faces in Dataset with medical masks. In comparison to other
approaches to solving this problem, our strategy showed to be successful and promising with a recognition accuracy
for "Extended Yale B" of 95%.
Downloads
References
A. K. Bhadani and A. Sinha, “A FACEMASK DETECTOR USING MACHINE LEARNING AND IMAGE PROCESSING Engineering Science and Technology , an International Journal A FACEMASK DETECTOR USING MACHINE LEARNING AND IMAGE PROCESSING,” Eng. Sci. Technol. an Int. J., no. November, pp. 1–8, 2020.
B. Kocacinar, B. Tas, and F. P. Akbulut, “A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System,” IEEE Access, vol. 10, pp. 63496–63507, 2022, doi: 10.1109/ACCESS.2022.3182055. DOI: https://doi.org/10.1109/ACCESS.2022.3182055
V. Pann and H. J. Lee, “Effective Attention-BasedMechanism for Masked Face Recognition,” Appl. Sci., vol. 12, no. 11, 2022, doi: 10.3390/app12115590. DOI: https://doi.org/10.3390/app12115590
P. I. Fatema, A. Khan, A. Gedekar, A. Khawaja, M. Barghat, and N. Khan, “Masked Face Recognition,” vol. 6, no. 1, pp. 98–102, 2021, doi: 10.48175/IJARSCT-1340. DOI: https://doi.org/10.48175/IJARSCT-1340
Y. Li, K. Guo, Y. Lu, and L. Liu, “Cropping and attention based approach for masked face recognition,” pp. 3012–3025, 2021. DOI: https://doi.org/10.1007/s10489-020-02100-9
H. N. Vu, M. H. Nguyen, and C. Pham, “Masked face recognition with convolutional neural networks and local binary patterns,” Appl. Intell., vol. 52, no. 5, pp. 5497–5512, 2022, doi: 10.1007/s10489-021-02728-1. DOI: https://doi.org/10.1007/s10489-021-02728-1
N. Ud Din, K. Javed, S. Bae, and J. Yi, “A Novel GAN-Based Network for Unmasking of Masked Face,” IEEE Access, vol. 8, pp. 44276–44287, 2020, doi: 10.1109/ACCESS.2020.2977386. DOI: https://doi.org/10.1109/ACCESS.2020.2977386
M. S. Ejaz and M. R. Islam, “Masked face recognition using convolutional neural network,” 2019 Int. Conf. Sustain. Technol. Ind. 4.0, STI 2019, no. December 2019, 2019, doi: 10.1109/STI47673.2019.9068044. DOI: https://doi.org/10.1109/STI47673.2019.9068044
M. Geng, “Masked Face Recognition with Generative Data Augmentation and Domain Constrained Ranking,” pp. 2246–2254, 2020. DOI: https://doi.org/10.1145/3394171.3413723
Q. Hong, Z. Wang, Z. He, N. Wang, X. Tian, and T. Lu, “Masked Face Recognition with Identification Association,” pp. 731–735, 2020, doi: 10.1109/ICTAI50040.2020.00116. DOI: https://doi.org/10.1109/ICTAI50040.2020.00116
B. Mandal, “Masked Face Recognition using ResNet-50,” 2012.
S. Malakar, W. Chiracharit, K. Chamnongthai and T. Charoenpong, "Masked Face Recognition Using Principal component analysis and Deep learning," 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Mai, Thailand, 2021, pp. 785-788, doi: 10.1109/ECTI-CON51831.2021.9454857. DOI: https://doi.org/10.1109/ECTI-CON51831.2021.9454857
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629, 2018, doi: 10.1007/s13244-018-0639-9. DOI: https://doi.org/10.1007/s13244-018-0639-9
X. Yang, M. Li, and S. L. Zhao, “Facial expression recognition algorithm based on CNN and LBP feature fusion,” ACM Int. Conf. Proceeding Ser., pp. 33–38, 2017, doi: 10.1145/3175603.3175615. DOI: https://doi.org/10.1145/3175603.3175615
P. Ke, M. Cai, H. Wang, and J. Chen, “A novel face recognition algorithm based on the combination of LBP and CNN,” Int. Conf. Signal Process. Proceedings, ICSP, vol. 2018-Augus, pp. 539–543, 2019, doi: 10.1109/ICSP.2018.8652477. DOI: https://doi.org/10.1109/ICSP.2018.8652477
S. Koul and U. Singhania, “Flower Species Detection Over Large Dataset Using Convolution Neural Networks and Image Processing Techniques,” Int. J. Innov. Sci. Res. Technol., vol. 5, no. 8, pp. 1722–1727,2020, doi: 10.38124/ijisrt20aug006. DOI: https://doi.org/10.38124/IJISRT20AUG006
A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: Illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 643–660, 2001, doi: 10.1109/34.927464. DOI: https://doi.org/10.1109/34.927464
M. Kumar and R. Mann, “Masked Face Recognition using Deep Learning Model,” Proc. - 2021 3rd Int. Conf. Adv. Comput. Commun. Control Networking, ICAC3N 2021, pp. 428–432, 2021, doi: 10.1109/ICAC3N53548.2021.9725368. DOI: https://doi.org/10.1109/ICAC3N53548.2021.9725368
Q. Zhu, P. Zhang, Z. Wang, and X. Ye, “A New Loss Function for CNN Classifier Based on Predefined Evenly-Distributed Class Centroids,” IEEE Access, vol. 8, pp. 10888–10895, 2020, doi: 10.1109/ACCESS.2019.2960065. DOI: https://doi.org/10.1109/ACCESS.2019.2960065
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 Saja Mohsen Abass
This work is licensed under a Creative Commons Attribution 4.0 International License.
which allows users to copy, create extracts, abstracts, and new works from the Article, alter and revise the Article, and make commercial use of the Article (including reuse and/or resale of the Article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made and the licensor is not represented as endorsing the use made of the work.
