Masked face recognition embarks the interest among the researchers to find a better algorithm to improve the performance of face recognition applications, especially in the Covid-19 pandemic lately. This paper introduces a proposed masked face recognition method known as Principal Random Forest Convolutional Neural Network (PRFCNN). This method utilizes the strengths of Principal Component Analysis (PCA) with the combination of Random Forest algorithm in Convolution Neural Network to pre-train the masked face features. PRFCNN is designed to assist in extracting more salient features and prevent overfitting problems. Experiments are conducted on two benchmarked datasets, RMFD (Real-World Masked Face Dataset) and LFW Simulated Masked Face Dataset using various parameter settings. The experimental result with a minimum recognition rate of 90% accuracy promises the effectiveness of the proposed PRFCNN over the other state-of-the-art methods.
HaririW. Efficient Masked Face Recognition Method during the COVID-19 Pandemic, no. July, 2021, doi: 10.21203/rs.3.rs-39289/v1.
2.
EjazM.S., IslamM.R., Masked face recognition using convolutional neural network, 2019 Int Conf Sustain Technol Ind 4.0, STI 2019, no. May, 2019, doi: 10.1109/STI47673.2019.9068044.
3.
MandalB., OkeukwuA., TheisY., Masked Face Recognition using ResNet-50, 2021, [Online]. Available: http://arxiv.org/abs/2104.08997.
4.
FatemaP.I., KhanA., GedekarA., KhawajaA., BarghatM., KhanN., “Masked Face Recognition, Int J Adv Res Sci Commun Technol, (2021), 98–102, doi: 10.48175/ijarsct-1340.
5.
WuG., Masked Face Recognition Algorithm for a Contactless Distribution Cabinet, Math Probl Eng2021, 2021. doi: 10.1155/2021/5591020.
6.
DesaiA., Applying Deep learning techniques - Masked facial recognition in Smartphone security systems using transfer learning MSc in Data Analytics at, Adarsh Desai, (2020), 1–48.
7.
Alzu’biA., AlbalasF., Al-HadhramiT., YounisL.B., BashayrehA., “Electronics Masked Face Recognition Using Deep Learning: A Review, 2021, doi: 10.3390/electronics10212666.
8.
DuH., ShiH., LiuY., ZengD. and MeiT., Towards NIR-VIS Masked Face Recognition, IEEE Signal Process Lett28 (2021), 768–772. doi: 10.1109/LSP.2021.3071663.
9.
DingF., PengP., HuangY., GengM., TianY., Masked Face Recognition with Latent Part Detection, MM 2020 - Proc 28th ACMInt Conf Multimed, (2020), 2281–2289. doi: 10.1145/3394171.3413731.
10.
AnwarA., RaychowdhuryA., Masked Face Recognition for Secure Authentication.
11.
KaurR., HimanshiE., Face recognition using Principal Component Analysis, Souvenir 2015 IEEE Int Adv Comput Conf IACC 2015, no. November, (2015), 585–589, doi: 10.1109/IADCC.2015.7154774.
TharwatA., Linear Discriminant Analysis: An Overview, 30(January 2015) (2017), 169–190.
14.
LiM., LuoX., YangJ. and SunY., Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG, J Sensors20162016. doi: 10.1155/2016/7481946.
IslamM.S., HossainE., RahmanA., HossainM.S. and AnderssonK., A review on recent advancements in FOREX currency prediction, Algorithms13(8) (2020), 1–23. doi: 10.3390/A13080186.
KisacaninB., Deep Learning for Autonomous Vehicles, (2017), 142–142, doi: 10.1109/ismvl.2017.49.
19.
VoulodimosA., DoulamisN., DoulamisA., ProtopapadakisE., Doulamis and E. Protopapadakis, Deep Learning for Computer Vision: A Brief Review, 2018, doi: 10.1155/2018/7068349.
20.
SünderhaufN., et al., The limits and potentials of deep learning for robotics, Int J Rob Res37(4–5) (2018), 405–420. doi: 10.1177/0278364918770733.
21.
SherstinskyA., Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, Phys D Nonlinear Phenom404 (2020), 1–43. doi: 10.1016/j.physd.2019.132306.
AsanU., AnIntroduction to Self-Organizing Maps An Introduction to Self-Organizing Maps, no. January, 2012, doi: 10.2991/978-94-91216-77-0.
24.
PengC.Y.J., LeeK.L. and IngersollG.M., An introduction to logistic regression analysis and reporting, J Educ Res96(1) (2002), 3–14. doi: 10.1080/00220670209598786.
StatnikovA., AliferisC.F., Are Random Forests Better than SupportVector Machines for Microarray-Based Cancer Classification?, 2007, Accessed: Feb. 18, 2022. [Online]. Available: http://www.stat.
31.
GoelR., MehmoodI., UgailH., A Study of Deep Learning-Based Face Recognition Models for Sibling Identification, 2021.
32.
DengH., FengZ., QianG., LvX., LiH. and LiG., MFCosface: a masked-face recognition algorithm based on large margin cosine loss, Appl Sci11(16) , 2021. doi: 10.3390/app11167310.
