An Approach of Iris Feature Extraction for Personal Identification
Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has b...
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creator | Patil, Chandrashekar M. Kulkarni, Sudarshan Patil |
description | Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. Features extracted using higher level wavelet decompositions are shown to yield better clustering and higher success rate in recognition. |
doi_str_mv | 10.1109/ARTCom.2009.14 |
format | Conference Proceeding |
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The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. 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The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. Features extracted using higher level wavelet decompositions are shown to yield better clustering and higher success rate in recognition.</description><subject>Biometric identification</subject><subject>Biometrics</subject><subject>Educational institutions</subject><subject>Eyelids</subject><subject>Feature extraction</subject><subject>Feature representation</subject><subject>Filters</subject><subject>Humans</subject><subject>Image edge detection</subject><subject>Image recognition</subject><subject>Iris recognition</subject><subject>Wavelet transforms</subject><isbn>9781424451043</isbn><isbn>1424451043</isbn><isbn>0769538452</isbn><isbn>9780769538457</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjs1KxDAYRSMiqDPdunGTF2jNf9JlLTNaGFCGcT0kzReMzDQlraBvb0Xv5sI9cDkI3VFSUUrqh2Z_aNO5YoTUFRUX6JZoVUtuhGSXqKi1oYIJISkR_BoV0_RBlizQEHWDHpsBN-OYk-3fcQq4y3HCW7DzZwa8-Zqz7eeYBhxSxq-QpzTYE-48DHMMsbe_bI2ugj1NUPz3Cr1tN4f2udy9PHVtsyt7xshcgnaLLWGBCkE9GGoXY8UMKK09I8YtkwkiSO80gFdUWQvcO9bzWjvm-Ard__1GADiOOZ5t_j5KzgyVmv8Ay59Kmw</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Patil, Chandrashekar M.</creator><creator>Kulkarni, Sudarshan Patil</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200910</creationdate><title>An Approach of Iris Feature Extraction for Personal Identification</title><author>Patil, Chandrashekar M. ; Kulkarni, Sudarshan Patil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c220t-e7b10902f1441de81a200628e677d208be818f4f5db7eed616aae3db2c397b2b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Biometric identification</topic><topic>Biometrics</topic><topic>Educational institutions</topic><topic>Eyelids</topic><topic>Feature extraction</topic><topic>Feature representation</topic><topic>Filters</topic><topic>Humans</topic><topic>Image edge detection</topic><topic>Image recognition</topic><topic>Iris recognition</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Patil, Chandrashekar M.</creatorcontrib><creatorcontrib>Kulkarni, Sudarshan Patil</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Patil, Chandrashekar M.</au><au>Kulkarni, Sudarshan Patil</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An Approach of Iris Feature Extraction for Personal Identification</atitle><btitle>2009 International Conference on Advances in Recent Technologies in Communication and Computing</btitle><stitle>ARTCOM</stitle><date>2009-10</date><risdate>2009</risdate><spage>796</spage><epage>799</epage><pages>796-799</pages><isbn>9781424451043</isbn><isbn>1424451043</isbn><eisbn>0769538452</eisbn><eisbn>9780769538457</eisbn><abstract>Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. Features extracted using higher level wavelet decompositions are shown to yield better clustering and higher success rate in recognition.</abstract><pub>IEEE</pub><doi>10.1109/ARTCom.2009.14</doi><tpages>4</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Biometric identification Biometrics Educational institutions Eyelids Feature extraction Feature representation Filters Humans Image edge detection Image recognition Iris recognition Wavelet transforms |
title | An Approach of Iris Feature Extraction for Personal Identification |
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