基于线性核熵分量分析的手指静脉识别

2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing Pub Date : 2012-11-26 DOI:10.1109/ICCP.2012.6356194

S. Damavandinejadmonfared

{"title":"基于线性核熵分量分析的手指静脉识别","authors":"S. Damavandinejadmonfared","doi":"10.1109/ICCP.2012.6356194","DOIUrl":null,"url":null,"abstract":"Based on the previous research, Kernel Entropy Component Analysis (KECA) is introduced as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face recognition. In this paper, an algorithm using KECA is proposed to merit finger vein recognition. The proposed algorithm is then compared to Principal Component Analysis (PCA) and Different types of KECA in order to determine the most appropriate one in terms of finger vein recognition.","PeriodicalId":406461,"journal":{"name":"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Finger vein recognition using linear Kernel Entropy Component Analysis\",\"authors\":\"S. Damavandinejadmonfared\",\"doi\":\"10.1109/ICCP.2012.6356194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the previous research, Kernel Entropy Component Analysis (KECA) is introduced as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face recognition. In this paper, an algorithm using KECA is proposed to merit finger vein recognition. The proposed algorithm is then compared to Principal Component Analysis (PCA) and Different types of KECA in order to determine the most appropriate one in terms of finger vein recognition.\",\"PeriodicalId\":406461,\"journal\":{\"name\":\"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCP.2012.6356194\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCP.2012.6356194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 13

摘要

在前人研究的基础上，提出了一种比核主成分分析更适合人脸识别的方法——核熵成分分析(Kernel Entropy Component Analysis, KECA)。本文提出了一种基于kea的手指静脉识别算法。然后将所提出的算法与主成分分析(PCA)和不同类型的kea进行比较，以确定最适合手指静脉识别的算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Finger vein recognition using linear Kernel Entropy Component Analysis

Based on the previous research, Kernel Entropy Component Analysis (KECA) is introduced as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face recognition. In this paper, an algorithm using KECA is proposed to merit finger vein recognition. The proposed algorithm is then compared to Principal Component Analysis (PCA) and Different types of KECA in order to determine the most appropriate one in terms of finger vein recognition.

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

2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing

自引率

0.00%

发文量