{"title":"基于lltsa的混合特征提取与降维静脉模式识别","authors":"P. Gopinath, R. Shivakumar","doi":"10.1080/13682199.2023.2257539","DOIUrl":null,"url":null,"abstract":"ABSTRACTIn information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods.KEYWORDS: Grey-level co-occurrence matrixhistogram of oriented gradientlocal binary patternstationary wavelet transformsupport vector machine Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsP. GopinathDr P. Gopinath is working as an Assistant Professor in the department of Electronics and Communication Engineering at Sengunthar Engineering College, Tiruchengode. He obtained his Ph. D in Digital Image Processing from Anna University Chennai in 2023. M.E. (Applied Electronics) from Anna University Chennai in 2011. B.E. (Electronics and Communication Engineering) from Anna University Chennai in 2008.He has a 13 years of teaching experience. His research interest includes Digital Image processing, Signal processing, Biometrics, Machine learning, and Artificial Intelligence. He has published more than 12 research article and 2 patent.R. ShivakumarDr R. Shivakumar is working as a Professor in Department of Electrical and Electronics Engineering at Sona College of Technology, Salem. He obtained his Ph. D in Electrical Engineering from Anna University Chennai in November 2012.M.E. (Power System Engg) -First class with Distinction in 1998 from Annamalai University, Chidambaram. B.E. (Electrical and Electronics Engineering) with I Class in 1997, from Shanmugha College of Engineering, Tanjore, Bharadhidasan University. His research interest includes Power System Stability and Control, Bio Inspired Optimization algorithms, Renewable energy conversion systems, and Digital Technology applications in Power Engineering. He has published more than 40 research article and 60 International and National Conference Papers. He won BEST RESEARCHER Award for academic contribution in Electrical and Electronics Engineering specialization under National Faculty Award 2021-2022 awarded by Novel Research Academy, Puducherry, India on 4.5.2022.","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid feature extraction and LLTSA-based dimension reduction for vein pattern recognition\",\"authors\":\"P. Gopinath, R. Shivakumar\",\"doi\":\"10.1080/13682199.2023.2257539\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTIn information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods.KEYWORDS: Grey-level co-occurrence matrixhistogram of oriented gradientlocal binary patternstationary wavelet transformsupport vector machine Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsP. GopinathDr P. Gopinath is working as an Assistant Professor in the department of Electronics and Communication Engineering at Sengunthar Engineering College, Tiruchengode. He obtained his Ph. D in Digital Image Processing from Anna University Chennai in 2023. M.E. (Applied Electronics) from Anna University Chennai in 2011. B.E. (Electronics and Communication Engineering) from Anna University Chennai in 2008.He has a 13 years of teaching experience. His research interest includes Digital Image processing, Signal processing, Biometrics, Machine learning, and Artificial Intelligence. He has published more than 12 research article and 2 patent.R. ShivakumarDr R. Shivakumar is working as a Professor in Department of Electrical and Electronics Engineering at Sona College of Technology, Salem. He obtained his Ph. D in Electrical Engineering from Anna University Chennai in November 2012.M.E. (Power System Engg) -First class with Distinction in 1998 from Annamalai University, Chidambaram. B.E. (Electrical and Electronics Engineering) with I Class in 1997, from Shanmugha College of Engineering, Tanjore, Bharadhidasan University. His research interest includes Power System Stability and Control, Bio Inspired Optimization algorithms, Renewable energy conversion systems, and Digital Technology applications in Power Engineering. He has published more than 40 research article and 60 International and National Conference Papers. 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引用次数: 0
摘要
摘要在信息安全领域,个人身份识别变得越来越重要。为了提高安全性,采用了几种生物特征识别技术。然而,在手指静脉识别中,它面临着假手指静脉图像、安全性和准确性不高的关键问题。为了解决这一问题,提出了基于线性局部切线空间对齐的混合特征提取和支持向量机分类器(HFE-LLTSA-SVM)。在该混合特征中,FE被认为是结合定向梯度直方图(HOG)、灰度共生矩阵(GLCM)、平稳小波变换(SWT)和局部二值模式(LBP)来提取混合特征。LLTSA在HOG、GLCM和LBP的HFE输出中执行降维。在此基础上,采用支持向量机进行分类,基于纠错码进行认证。最后对性能参数进行了计算,与现有方法相比,该方法的准确率达到了99.75%。关键词:灰度共现矩阵定向梯度直方图局部二值模式平稳小波变换支持向量机披露声明作者未报告潜在利益冲突。附加信息:贡献者说明Gopinath博士是Tiruchengode Sengunthar工程学院电子与通信工程系的助理教授。他于2023年获得金奈安娜大学数字图像处理博士学位。2011年毕业于金奈安娜大学应用电子学硕士。2008年毕业于金奈安娜大学电子与通信工程学士学位。他有13年的教学经验。主要研究方向为数字图像处理、信号处理、生物识别、机器学习、人工智能等。发表学术论文12篇,专利2项。R. Shivakumar博士是塞勒姆Sona理工学院电气和电子工程系的教授。他于2012年11月获得Anna University Chennai电气工程博士学位。(电力系统工程)- 1998年毕业于印度奇丹巴拉姆的安纳玛莱大学,获一等优异成绩。1997年毕业于印度Bharadhidasan大学Shanmugha工程学院,获电气与电子工程学士学位。主要研究方向为电力系统稳定性与控制、生物优化算法、可再生能源转换系统、数字技术在电力工程中的应用。发表研究论文40余篇,国际国内会议论文60余篇。他于2022年5月4日获得了由印度普杜切里新颖研究学院颁发的2021-2022年国家教师奖的电气和电子工程专业学术贡献奖。
Hybrid feature extraction and LLTSA-based dimension reduction for vein pattern recognition
ABSTRACTIn information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods.KEYWORDS: Grey-level co-occurrence matrixhistogram of oriented gradientlocal binary patternstationary wavelet transformsupport vector machine Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsP. GopinathDr P. Gopinath is working as an Assistant Professor in the department of Electronics and Communication Engineering at Sengunthar Engineering College, Tiruchengode. He obtained his Ph. D in Digital Image Processing from Anna University Chennai in 2023. M.E. (Applied Electronics) from Anna University Chennai in 2011. B.E. (Electronics and Communication Engineering) from Anna University Chennai in 2008.He has a 13 years of teaching experience. His research interest includes Digital Image processing, Signal processing, Biometrics, Machine learning, and Artificial Intelligence. He has published more than 12 research article and 2 patent.R. ShivakumarDr R. Shivakumar is working as a Professor in Department of Electrical and Electronics Engineering at Sona College of Technology, Salem. He obtained his Ph. D in Electrical Engineering from Anna University Chennai in November 2012.M.E. (Power System Engg) -First class with Distinction in 1998 from Annamalai University, Chidambaram. B.E. (Electrical and Electronics Engineering) with I Class in 1997, from Shanmugha College of Engineering, Tanjore, Bharadhidasan University. His research interest includes Power System Stability and Control, Bio Inspired Optimization algorithms, Renewable energy conversion systems, and Digital Technology applications in Power Engineering. He has published more than 40 research article and 60 International and National Conference Papers. He won BEST RESEARCHER Award for academic contribution in Electrical and Electronics Engineering specialization under National Faculty Award 2021-2022 awarded by Novel Research Academy, Puducherry, India on 4.5.2022.