CFP last date
20 June 2024
Reseach Article

Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model

by Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 2
Year of Publication: 2024
Authors: Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam

Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam . Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model. International Journal of Computer Applications. 186, 2 ( Jan 2024), 42-51. DOI=10.5120/ijca2024923351

@article{ 10.5120/ijca2024923351,
author = { Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam },
title = { Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 2 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 42-51 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2024923351 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:29:33.031319+05:30
%A Md. Omar Faruque
%A Md. Rashedul Islam
%A Md. Touhid Islam
%T Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 2
%P 42-51
%D 2024
%I Foundation of Computer Science (FCS), NY, USA

For public health and safety reasons, face masks were required worldwide during the COVID-19 epidemic. However, this poses challenges for face recognition systems as the face is partially covered. Face recognition is a widely used and cost-effective biometric security system, but it faces difficulties in accurately identifying individuals wearing masks. Existing algorithms for face recognition have struggled to maintain efficiency, accuracy, and performance in the context of masked faces. To address these challenges and improve cost-effectiveness, a new machine learning model is required. This manuscript describes a lightweight deep learning methodology that is flexible and efficient in recognizing masked faces. The HSTU Masked Face Dataset (HMFD) is utilized, comprising frontal and lateral faces with various colored masks. Our proposed method involves a lightweight CNN model designed to enhance the accuracy of masked face identification. To enhance operational efficiency, methods like batch normalization, dropout, and depth-wise normalization are integrated which are tailored to meet particular specifications, aiming to optimize overall performance. These techniques improve the efficiency and accuracy of the model while minimizing overall complexity. In this research, the accuracy of the model is evaluated in comparison to other well-established deep learning models, including VGG16, VGG19, Extended VGG19, MobileNet, and MobileNetV2. The results demonstrate that our lightweight deep learning model outperforms these models, achieving a high recognition accuracy of 97%. By considering the needs of the task and carefully optimizing the model architecture, our proposed method offers an effective and efficient solution for recognizing masked faces in real-world scenarios.

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Index Terms

Computer Science
Information Sciences


Masked Face Recognition Deep Learning Convolutional Neural Network Max-pooling Lightweight CNN Covid-19 Pandemic