{"title":"Masked Faces Recognition Using Deep Learning Models and the Structural Similarity Measure","authors":"Ouahab Abdelwhab","doi":"10.3103/s8756699023060146","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Wearing a mask is an important element to prevent infection with Corona disease. With the widespread adoption of face masks as a preventive measure, traditional face recognition systems encounter challenges in accurately identifying individuals. In this paper, we proposed a methodology that uses different deep learning models with pretrained weights on ImageNet to extract features and the structural similarity measure (SSIM) to recognize masked faces. Ten deep learning models were used, which are VGG16, VGG19, ReseNet50, Inception, InpectionV3, MobileNet, DenseNet201, NasNetMobile, EfficientNetB7, and InceptionResNet. The classification accuracy is used to evaluate the performance of each model. VGG-16, VGG-19, MobileNet and EfficientNetB7 gave the best results with an accuracy of 98<span>\\(\\%\\)</span> which means that these methods are more appropriate for masked face recognition.</p>","PeriodicalId":44919,"journal":{"name":"Optoelectronics Instrumentation and Data Processing","volume":"22 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronics Instrumentation and Data Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s8756699023060146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Wearing a mask is an important element to prevent infection with Corona disease. With the widespread adoption of face masks as a preventive measure, traditional face recognition systems encounter challenges in accurately identifying individuals. In this paper, we proposed a methodology that uses different deep learning models with pretrained weights on ImageNet to extract features and the structural similarity measure (SSIM) to recognize masked faces. Ten deep learning models were used, which are VGG16, VGG19, ReseNet50, Inception, InpectionV3, MobileNet, DenseNet201, NasNetMobile, EfficientNetB7, and InceptionResNet. The classification accuracy is used to evaluate the performance of each model. VGG-16, VGG-19, MobileNet and EfficientNetB7 gave the best results with an accuracy of 98\(\%\) which means that these methods are more appropriate for masked face recognition.
期刊介绍：
The scope of Optoelectronics, Instrumentation and Data Processing encompasses, but is not restricted to, the following areas: analysis and synthesis of signals and images; artificial intelligence methods; automated measurement systems; physicotechnical foundations of micro- and optoelectronics; optical information technologies; systems and components; modelling in physicotechnical research; laser physics applications; computer networks and data transmission systems. The journal publishes original papers, reviews, and short communications in order to provide the widest possible coverage of latest research and development in its chosen field.
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