人脸识别中的变换域技术综述

Taif Alobaidi, W. Mikhael
{"title":"人脸识别中的变换域技术综述","authors":"Taif Alobaidi, W. Mikhael","doi":"10.1109/MWSCAS47672.2021.9531795","DOIUrl":null,"url":null,"abstract":"In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"23 1","pages":"246-249"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Review Paper on Transform Domains Techniques for Face Recognition\",\"authors\":\"Taif Alobaidi, W. Mikhael\",\"doi\":\"10.1109/MWSCAS47672.2021.9531795\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.\",\"PeriodicalId\":6792,\"journal\":{\"name\":\"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)\",\"volume\":\"23 1\",\"pages\":\"246-249\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSCAS47672.2021.9531795\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS47672.2021.9531795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在过去的几年里,我们发表了几篇论文来解决人脸识别问题。这些文章中使用的技术是在转换域中实现的。利用离散余弦(DCT)和离散小波(DWT)变换,无论是组合还是单独,来提取特征,形成给定数据集中每个参与者的最终模型。在本文中,我们强调了我们以前工作的重要部分,以便在所有方法之间进行公平的比较。这里包括以下数据集的结果:ORL, YALE, FERET, FEI, Georgia Tech和裁剪AR。特征是DWT, DCT,基于能量的选择DCT-DWT和组合DCT-DWT系数,而分类器是欧氏距离,平方或幂为1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Review Paper on Transform Domains Techniques for Face Recognition
In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.
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
期刊最新文献
Hybrid Frequency Domain Simulation Method to Speed-up Analysis of Injection Locked Oscillators SaFIoV: A Secure and Fast Communication in Fog-based Internet-of-Vehicles using SDN and Blockchain Capacitor-Less Memristive Integrate-and-Fire Neuron with Stochastic Behavior Polynomial Filters with Controllable Overshoot In Their Step Transient Responses A low kickback noise and low power dynamic comparator
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1