基于多光谱成像的鲁棒性别分类

N. Vetrekar, Ramachandra Raghavendra, K. Raja, R. Gad, C. Busch
{"title":"基于多光谱成像的鲁棒性别分类","authors":"N. Vetrekar, Ramachandra Raghavendra, K. Raja, R. Gad, C. Busch","doi":"10.1109/SITIS.2017.46","DOIUrl":null,"url":null,"abstract":"Multi-Spectral imaging is gaining importance in recent times due to it's ability to capture spatio-spectral data across the electromagnetic spectrum. In this paper, we present a robust gender classification approach by exploring the inherent properties of multi-spectral imaging sensor. We propose a framework that processes the spectral data independently using Spectral Angle Mapper (SAM) and Discrete Wavelet Transform (DCT), which are further combined to learn in a linear Support Vector Machine (SVM) classifier, the gender prediction. We present an extensive set of experimental results in the form of average classification accuracy using multi-spectral face database of 78300 samples images corresponding to 145 subjects in six different illumination conditions. The highest average classification accuracy of 96.80±1.60% is obtained using proposed approach signifying the potential of multi-spectral imaging for robust gender classification.","PeriodicalId":153165,"journal":{"name":"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Robust Gender Classification Using Multi-Spectral Imaging\",\"authors\":\"N. Vetrekar, Ramachandra Raghavendra, K. Raja, R. Gad, C. Busch\",\"doi\":\"10.1109/SITIS.2017.46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-Spectral imaging is gaining importance in recent times due to it's ability to capture spatio-spectral data across the electromagnetic spectrum. In this paper, we present a robust gender classification approach by exploring the inherent properties of multi-spectral imaging sensor. We propose a framework that processes the spectral data independently using Spectral Angle Mapper (SAM) and Discrete Wavelet Transform (DCT), which are further combined to learn in a linear Support Vector Machine (SVM) classifier, the gender prediction. We present an extensive set of experimental results in the form of average classification accuracy using multi-spectral face database of 78300 samples images corresponding to 145 subjects in six different illumination conditions. The highest average classification accuracy of 96.80±1.60% is obtained using proposed approach signifying the potential of multi-spectral imaging for robust gender classification.\",\"PeriodicalId\":153165,\"journal\":{\"name\":\"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITIS.2017.46\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2017.46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

近年来,多光谱成像越来越重要,因为它能够捕获跨电磁频谱的空间光谱数据。本文通过探索多光谱成像传感器的固有特性,提出了一种鲁棒的性别分类方法。我们提出了一个使用光谱角映射器(SAM)和离散小波变换(DCT)独立处理光谱数据的框架,并将其进一步结合在线性支持向量机(SVM)分类器中学习性别预测。在6种不同光照条件下,我们利用多光谱人脸数据库中对应145个受试者的78300张样本图像,以平均分类精度的形式给出了一组广泛的实验结果。使用该方法获得的最高平均分类准确率为96.80±1.60%,这表明多光谱成像具有鲁棒性性别分类的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Gender Classification Using Multi-Spectral Imaging
Multi-Spectral imaging is gaining importance in recent times due to it's ability to capture spatio-spectral data across the electromagnetic spectrum. In this paper, we present a robust gender classification approach by exploring the inherent properties of multi-spectral imaging sensor. We propose a framework that processes the spectral data independently using Spectral Angle Mapper (SAM) and Discrete Wavelet Transform (DCT), which are further combined to learn in a linear Support Vector Machine (SVM) classifier, the gender prediction. We present an extensive set of experimental results in the form of average classification accuracy using multi-spectral face database of 78300 samples images corresponding to 145 subjects in six different illumination conditions. The highest average classification accuracy of 96.80±1.60% is obtained using proposed approach signifying the potential of multi-spectral imaging for robust gender classification.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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