Using empirical mode decomposition for iris recognition
Iris recognition is known as an inherently reliable technique for human identification. Empirical Mode Decomposition (EMD), an adaptive multi-resolution decomposition technique, appears to be suitable for non-linear, non-stationary data analysis. Based on EMD, a fully data-driven method without usin...
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| Veröffentlicht in: | Computer standards and interfaces 2009-06, Vol.31 (4), p.729-739 |
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| container_title | Computer standards and interfaces |
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| creator | Chang, Chien-Ping Lee, Jen-Chun Su, Yu Huang, Ping S. Tu, Te-Ming |
| description | Iris recognition is known as an inherently reliable technique for human identification. Empirical Mode Decomposition (EMD), an adaptive multi-resolution decomposition technique, appears to be suitable for non-linear, non-stationary data analysis. Based on EMD, a fully data-driven method without using any pre-determined filter or wavelet function, an iris recognition scheme is presented by modifying EMD as a low-pass filter to analyze the iris images. To evaluate the efficacy of the proposed approach, three different similarity measures are used. Experimental results show that three metrics have all achieved promising and similar performance. Therefore, the proposed method demonstrates to be feasible for iris recognition and EMD is suitable for feature extraction. |
| doi_str_mv | 10.1016/j.csi.2008.09.013 |
| format | Article |
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Therefore, the proposed method demonstrates to be feasible for iris recognition and EMD is suitable for feature extraction.</description><subject>Biometrics</subject><subject>C (programming language)</subject><subject>Decomposition</subject><subject>Empirical analysis</subject><subject>Empirical Mode Decomposition (EMD)</subject><subject>Iris recognition</subject><subject>Mathematical analysis</subject><subject>Multi-resolution decomposition</subject><subject>Recognition</subject><subject>Similarity</subject><issn>0920-5489</issn><issn>1872-7018</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhC0EEqXwA7jlxilh_UjsiBOqeEmVuNCz5dqbylUSBztF4t_jUs6cVpqdWe18hNxSqCjQ5n5f2eQrBqAqaCug_IwsqJKslEDVOVlAy6CshWovyVVKewBgDZcLIjfJj7sCh8lHb01fDMFh4dCGYQrJzz6MRRdikbepiFnejb_iNbnoTJ_w5m8uyeb56WP1Wq7fX95Wj-vSctbOJWeU14Iqbq1Cse1kx63hjbPOgOjyuw04lJyjYlbUhotmC2CQqQ5V66DlS3J3ujvF8HnANOvBJ4t9b0YMh6RlzaWgTMjspCenjSGliJ2eoh9M_NYU9JGR3uvMSB8ZaWh1ZpQzD6cM5gpfHqNO1uNo0fncddYu-H_SP6RYbtw</recordid><startdate>20090601</startdate><enddate>20090601</enddate><creator>Chang, Chien-Ping</creator><creator>Lee, Jen-Chun</creator><creator>Su, Yu</creator><creator>Huang, Ping S.</creator><creator>Tu, Te-Ming</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20090601</creationdate><title>Using empirical mode decomposition for iris recognition</title><author>Chang, Chien-Ping ; Lee, Jen-Chun ; Su, Yu ; Huang, Ping S. ; Tu, Te-Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c329t-321354183cc8e4bf7f3ca36dcda04f20060de733e82c45a346b00ae28fe89d093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Biometrics</topic><topic>C (programming language)</topic><topic>Decomposition</topic><topic>Empirical analysis</topic><topic>Empirical Mode Decomposition (EMD)</topic><topic>Iris recognition</topic><topic>Mathematical analysis</topic><topic>Multi-resolution decomposition</topic><topic>Recognition</topic><topic>Similarity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Chien-Ping</creatorcontrib><creatorcontrib>Lee, Jen-Chun</creatorcontrib><creatorcontrib>Su, Yu</creatorcontrib><creatorcontrib>Huang, Ping S.</creatorcontrib><creatorcontrib>Tu, Te-Ming</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computer standards and interfaces</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Chien-Ping</au><au>Lee, Jen-Chun</au><au>Su, Yu</au><au>Huang, Ping S.</au><au>Tu, Te-Ming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using empirical mode decomposition for iris recognition</atitle><jtitle>Computer standards and interfaces</jtitle><date>2009-06-01</date><risdate>2009</risdate><volume>31</volume><issue>4</issue><spage>729</spage><epage>739</epage><pages>729-739</pages><issn>0920-5489</issn><eissn>1872-7018</eissn><abstract>Iris recognition is known as an inherently reliable technique for human identification. Empirical Mode Decomposition (EMD), an adaptive multi-resolution decomposition technique, appears to be suitable for non-linear, non-stationary data analysis. Based on EMD, a fully data-driven method without using any pre-determined filter or wavelet function, an iris recognition scheme is presented by modifying EMD as a low-pass filter to analyze the iris images. To evaluate the efficacy of the proposed approach, three different similarity measures are used. Experimental results show that three metrics have all achieved promising and similar performance. Therefore, the proposed method demonstrates to be feasible for iris recognition and EMD is suitable for feature extraction.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.csi.2008.09.013</doi><tpages>11</tpages></addata></record> |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Biometrics C (programming language) Decomposition Empirical analysis Empirical Mode Decomposition (EMD) Iris recognition Mathematical analysis Multi-resolution decomposition Recognition Similarity |
| title | Using empirical mode decomposition for iris recognition |
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