Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks

Q2 Engineering
Mohamed Oulad-Kaddour, Hamid Haddadou, D. Palacios-Alonso, C. Conde, E. Cabello
{"title":"Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks","authors":"Mohamed Oulad-Kaddour, Hamid Haddadou, D. Palacios-Alonso, C. Conde, E. Cabello","doi":"10.4108/eetinis.v11i2.4318","DOIUrl":null,"url":null,"abstract":"The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"53 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eetinis.v11i2.4318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.
利用卷积神经网络进行戴面具预测和自适应性别分类
冠状病毒大流行给世界带来了一段特殊的时期。为了限制 Covid-19 的传播,各国政府要求人们外出时佩戴口罩。在人脸数据分析中,戴面具的人脸会遮挡重要的口腔区域,给人脸识别和分类带来更多挑战。考虑到戴面具的情况,对现有解决方案进行改进是研究人员不可或缺的工作。在本文中,我们提出了一种用于戴面具预测和自适应人脸性别分类的方法。该方法基于卷积神经网络(CNN)。戴面具和性别信息对于各种可能的应用都至关重要。实验表明,使用卷积神经网络可以很好地检测戴面具的情况,这也证明了将其作为前置步骤的合理性。实验还表明,要保持性别分类的性能,使用戴面具的人脸进行再训练是必不可少的。此外,实验证明,在可控的人脸姿态和可接受的图像质量背景下,性别属性仍然可以很好地检测出来。最后,我们通过实证证明,所提出的自适应方法提高了混合背景下性别预测的整体性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.00
自引率
0.00%
发文量
15
审稿时长
10 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信