计算机科学
面部识别系统
卷积神经网络
人工智能
生物识别
面子(社会学概念)
认证(法律)
移动设备
深度学习
访问控制
模式识别(心理学)
机器学习
计算机安全
万维网
社会科学
社会学
作者
Büşra Kocaçınar,Bilal Tas,Fatma Patlar Akbulut,Çağatay Çatal,Deepti Mishra
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 63496-63507
被引量:18
标识
DOI:10.1109/access.2022.3182055
摘要
Due 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, currently, there are not enough image datasets that contain 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 were 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.
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