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Evaluating the masked and unmasked face with LeNet algorithm

Face recognition is a biometric technique that has been widely used in many fields. Most of the face recognition applications are used in the security and surveillance system. However, due to current pandemic, wearing facemasks is an obligation for everybody in public places. Hence, face recognition...

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Main Authors:	Rusli, Muhammad Haziq, Sjarif, Nilam Nur Amir, Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz, Kok, Steven, Kadir, Muhammad Solihin
Format:	Conference or Workshop Item
Published:	2021
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/94383/ http://dx.doi.org/10.1109/CSPA52141.2021.9377283
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id	my.utm.94383
record_format	eprints
spelling	my.utm.943832022-03-31T15:21:31Z http://eprints.utm.my/id/eprint/94383/ Evaluating the masked and unmasked face with LeNet algorithm Rusli, Muhammad Haziq Sjarif, Nilam Nur Amir Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz Kok, Steven Kadir, Muhammad Solihin T Technology (General) Face recognition is a biometric technique that has been widely used in many fields. Most of the face recognition applications are used in the security and surveillance system. However, due to current pandemic, wearing facemasks is an obligation for everybody in public places. Hence, face recognition will encounter a problem such as extracting the facial features due to blockage caused by the facemasks. Thus, it will lower down the recognition rate level. The collected dataset consists of two categories which is masked face and unmasked face. This dataset called FaceMask Dataset was obtained from kaggle website. The Multi-Task Cascaded Neural Network (MTCNN) was used to find the face region in the dataset, and it will undergo image feature extraction and remove the undetected face as to prepare a proper training dataset before it can be trained by using LeNet algorithm. As the result, the categories in this work are fall into two classes, which masked face and unmasked face. The training accuracy was 98.21% and it will focus on the features on each result and will justify the difference between the accuracy for both classes. The proposed method was able to achieve 98.21% accuracy with LeNet algorithm as the face image was mainly focused on the face area without taking full size of the image. 2021 Conference or Workshop Item PeerReviewed Rusli, Muhammad Haziq and Sjarif, Nilam Nur Amir and Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz and Kok, Steven and Kadir, Muhammad Solihin (2021) Evaluating the masked and unmasked face with LeNet algorithm. In: 17th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2021, 5 - 6 March 2021, Langkawi, Malaysia. http://dx.doi.org/10.1109/CSPA52141.2021.9377283
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	T Technology (General)
spellingShingle	T Technology (General) Rusli, Muhammad Haziq Sjarif, Nilam Nur Amir Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz Kok, Steven Kadir, Muhammad Solihin Evaluating the masked and unmasked face with LeNet algorithm
description	Face recognition is a biometric technique that has been widely used in many fields. Most of the face recognition applications are used in the security and surveillance system. However, due to current pandemic, wearing facemasks is an obligation for everybody in public places. Hence, face recognition will encounter a problem such as extracting the facial features due to blockage caused by the facemasks. Thus, it will lower down the recognition rate level. The collected dataset consists of two categories which is masked face and unmasked face. This dataset called FaceMask Dataset was obtained from kaggle website. The Multi-Task Cascaded Neural Network (MTCNN) was used to find the face region in the dataset, and it will undergo image feature extraction and remove the undetected face as to prepare a proper training dataset before it can be trained by using LeNet algorithm. As the result, the categories in this work are fall into two classes, which masked face and unmasked face. The training accuracy was 98.21% and it will focus on the features on each result and will justify the difference between the accuracy for both classes. The proposed method was able to achieve 98.21% accuracy with LeNet algorithm as the face image was mainly focused on the face area without taking full size of the image.
format	Conference or Workshop Item
author	Rusli, Muhammad Haziq Sjarif, Nilam Nur Amir Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz Kok, Steven Kadir, Muhammad Solihin
author_facet	Rusli, Muhammad Haziq Sjarif, Nilam Nur Amir Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz Kok, Steven Kadir, Muhammad Solihin
author_sort	Rusli, Muhammad Haziq
title	Evaluating the masked and unmasked face with LeNet algorithm
title_short	Evaluating the masked and unmasked face with LeNet algorithm
title_full	Evaluating the masked and unmasked face with LeNet algorithm
title_fullStr	Evaluating the masked and unmasked face with LeNet algorithm
title_full_unstemmed	Evaluating the masked and unmasked face with LeNet algorithm
title_sort	evaluating the masked and unmasked face with lenet algorithm
publishDate	2021
url	http://eprints.utm.my/id/eprint/94383/ http://dx.doi.org/10.1109/CSPA52141.2021.9377283
_version_	1729703164479799296
score	13.211869

Evaluating the masked and unmasked face with LeNet algorithm

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