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AuthorKocacinar, Busra
AuthorTas, Bilal
AuthorAkbulut, Fatma Patlar
AuthorCatal, Cagatay
AuthorMishra, Deepti
Available date2022-11-30T11:23:20Z
Publication Date2022
Publication NameIEEE Access
ResourceScopus
Resource2-s2.0-85132764139
URIhttp://dx.doi.org/10.1109/ACCESS.2022.3182055
URIhttp://hdl.handle.net/10576/36796
AbstractDue to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus, it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, there are not enough image datasets containing a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application was developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals- 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches. 2022 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectConvolutional neural networks; deep learning; fine tuning; masked face recognition; TinyML; transfer learning
TitleA Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System
TypeArticle
Pagination63496-63507
Volume Number10


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