Recognition of Static Hand Gesture

Khadidja Sadeddine, R. Djeradi, F. Chelali, A. Djeradi
{"title":"Recognition of Static Hand Gesture","authors":"Khadidja Sadeddine, R. Djeradi, F. Chelali, A. Djeradi","doi":"10.1109/ICMCS.2018.8525908","DOIUrl":null,"url":null,"abstract":"Human-Human (deaf people-ordinary people) and Human-Machine communication have become an interesting area of research requiring robust recognition systems. The paper proposes an implementation of hand posture recognition using three databases (Arabic Sign Language ArSL, American Sign Language ASL, and NUS hand posture) under uniform background. For that Hu's invariant moments descriptor, Local Binary Pattern (LBP) descriptor, Zernike moments descriptor, and Generic Fourier descriptor (GFD) are employed for the image characterization. Classification task is based on neural networks. The paper implements the fusion of the descriptors in order to increase the performance. Best recognition rates are reached for American Language with 93.33% for LBPD and same accuracy for NUS dataset with GFD.","PeriodicalId":272255,"journal":{"name":"2018 6th International Conference on Multimedia Computing and Systems (ICMCS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Conference on Multimedia Computing and Systems (ICMCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMCS.2018.8525908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

Human-Human (deaf people-ordinary people) and Human-Machine communication have become an interesting area of research requiring robust recognition systems. The paper proposes an implementation of hand posture recognition using three databases (Arabic Sign Language ArSL, American Sign Language ASL, and NUS hand posture) under uniform background. For that Hu's invariant moments descriptor, Local Binary Pattern (LBP) descriptor, Zernike moments descriptor, and Generic Fourier descriptor (GFD) are employed for the image characterization. Classification task is based on neural networks. The paper implements the fusion of the descriptors in order to increase the performance. Best recognition rates are reached for American Language with 93.33% for LBPD and same accuracy for NUS dataset with GFD.
静态手势的识别
人-人(聋人-普通人)和人机交流已经成为一个有趣的研究领域,需要强大的识别系统。本文提出了一种在统一背景下,利用阿拉伯手语ArSL、美国手语ASL和NUS手势数据库实现手势识别的方法。采用Hu不变矩描述子、局部二值模式(LBP)描述子、Zernike矩描述子和通用傅立叶描述子(GFD)进行图像表征。分类任务是基于神经网络的。为了提高性能,本文实现了描述符的融合。LBPD对美国语言的识别率达到了93.33%,使用GFD对NUS数据集达到了相同的准确率。
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
自引率
0.00%
发文量
0
×
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