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Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network

by Chamandeep Vimal, Neeraj Shirivastava
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 50
Year of Publication: 2022
Authors: Chamandeep Vimal, Neeraj Shirivastava

Chamandeep Vimal, Neeraj Shirivastava . Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network. International Journal of Computer Applications. 183, 50 ( Feb 2022), 16-21. DOI=10.5120/ijca2022921700

@article{ 10.5120/ijca2022921700,
author = { Chamandeep Vimal, Neeraj Shirivastava },
title = { Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 50 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2022921700 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:15:23.183152+05:30
%A Chamandeep Vimal
%A Neeraj Shirivastava
%T Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 50
%P 16-21
%D 2022
%I Foundation of Computer Science (FCS), NY, USA

Face recognition can be used in several applications such as in surveillance, identification in login system and personalized technology. The challenge of the face detection system is the non-frontal face position and the use of accessories that cover the face area; even conventional detection systems that rely on facial features are difficult to get high accuracy. The proposed system can overcome these problems and it can detect human face with mask also. The deep learning system can recognize facial features with complex backgrounds. The VGG16 architecture based on convolutional neural network with shallow layers to produce light computing then the system can work real-time. Multiple layer detection on the last feature map is used to detect varied face sizes. The system result shows sequential images of face localization with 93% accuracy.

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Index Terms

Computer Science
Information Sciences


Face detection Real-time CPU Multiple layer Deep learning