基于面部图像的生物识别系统的创建

G. Dimitrov, Olexii Bychkov, P. Petrova, K. Merkulova, Y. Zhabska, E. Zaitseva, L. Adaryukova, Pavel Petrov
{"title":"基于面部图像的生物识别系统的创建","authors":"G. Dimitrov, Olexii Bychkov, P. Petrova, K. Merkulova, Y. Zhabska, E. Zaitseva, L. Adaryukova, Pavel Petrov","doi":"10.23919/SMAGRIMET48809.2020.9263995","DOIUrl":null,"url":null,"abstract":"This article is dedicated to the creation of a biometric system of the identification by facial image. The conceptual model and the mathematical model based on image processing methods (Gabor and Daubechies wavelet transform) were developed, also steps of image processing in the program and the forming of the feature vector by calculating the image statistical characteristics were described during the analysis. Considering that the primary efficiency outcome of the system is the identification accuracy, which is performed by the system, it became necessary to carry out the experiments to determine these parameters, specifically the methods that are appropriate to use in the system. The results of the experimentation indicated that the system performs the most accurate identification outcome with the use of Daubechies wavelet transform for the image processing, the standard deviation and the variance for the feature vector forming, and Euclidean distance, the squared Euclidean distance or the Canberra distance as a metric of the image classification. Using these parameters, the created system performed the 92,5% accuracy of identification.","PeriodicalId":272673,"journal":{"name":"2020 3rd International Colloquium on Intelligent Grid Metrology (SMAGRIMET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Creation of Biometric System of Identification by Facial Image\",\"authors\":\"G. Dimitrov, Olexii Bychkov, P. Petrova, K. Merkulova, Y. Zhabska, E. Zaitseva, L. Adaryukova, Pavel Petrov\",\"doi\":\"10.23919/SMAGRIMET48809.2020.9263995\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article is dedicated to the creation of a biometric system of the identification by facial image. The conceptual model and the mathematical model based on image processing methods (Gabor and Daubechies wavelet transform) were developed, also steps of image processing in the program and the forming of the feature vector by calculating the image statistical characteristics were described during the analysis. Considering that the primary efficiency outcome of the system is the identification accuracy, which is performed by the system, it became necessary to carry out the experiments to determine these parameters, specifically the methods that are appropriate to use in the system. The results of the experimentation indicated that the system performs the most accurate identification outcome with the use of Daubechies wavelet transform for the image processing, the standard deviation and the variance for the feature vector forming, and Euclidean distance, the squared Euclidean distance or the Canberra distance as a metric of the image classification. Using these parameters, the created system performed the 92,5% accuracy of identification.\",\"PeriodicalId\":272673,\"journal\":{\"name\":\"2020 3rd International Colloquium on Intelligent Grid Metrology (SMAGRIMET)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 3rd International Colloquium on Intelligent Grid Metrology (SMAGRIMET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/SMAGRIMET48809.2020.9263995\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Colloquium on Intelligent Grid Metrology (SMAGRIMET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/SMAGRIMET48809.2020.9263995","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

本文致力于建立一个基于人脸图像的生物识别系统。建立了基于图像处理方法(Gabor和Daubechies小波变换)的概念模型和数学模型,并在分析过程中描述了程序中图像处理的步骤以及通过计算图像统计特征形成特征向量。考虑到系统的主要效率结果是系统执行的识别精度,因此有必要进行实验来确定这些参数,特别是适合系统使用的方法。实验结果表明,采用Daubechies小波变换进行图像处理,以标准差和方差构成特征向量,以欧几里得距离、欧几里得距离的平方或堪培拉距离作为图像分类的度量,可以获得最准确的识别结果。使用这些参数,所创建的系统的识别准确率为92.5%。
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
Creation of Biometric System of Identification by Facial Image
This article is dedicated to the creation of a biometric system of the identification by facial image. The conceptual model and the mathematical model based on image processing methods (Gabor and Daubechies wavelet transform) were developed, also steps of image processing in the program and the forming of the feature vector by calculating the image statistical characteristics were described during the analysis. Considering that the primary efficiency outcome of the system is the identification accuracy, which is performed by the system, it became necessary to carry out the experiments to determine these parameters, specifically the methods that are appropriate to use in the system. The results of the experimentation indicated that the system performs the most accurate identification outcome with the use of Daubechies wavelet transform for the image processing, the standard deviation and the variance for the feature vector forming, and Euclidean distance, the squared Euclidean distance or the Canberra distance as a metric of the image classification. Using these parameters, the created system performed the 92,5% accuracy of identification.
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
A Review of Machine Learning Applications in Electricity Market Studies Proceedings of 2020 3rd International Colloquium on Intelligent Grid Metrology (SMAGRIMET 2020) A Review of Phasor Estimation Algorithms On Line Electromechanical Oscillations Detection in Transmission Network with Synchrophasor Power transformer sound pressure level spectra versus electrical current spectra: experimental findings
×
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