Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition
Iris recognition is an alternative authentication method. Many studies have tried to improve iris recognition as a biometric-based alternative for secure authentication. Iris segmentation is an important part of iris recognition because it defines the image region that is used for subsequent process...
Gespeichert in:
| Veröffentlicht in: | International journal of advanced computer science & applications 2020, Vol.11 (7) |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 7 |
| container_start_page | |
| container_title | International journal of advanced computer science & applications |
| container_volume | 11 |
| creator | Ihsanto, Eko Kurniawan, Jeffry Husna, Diyanatul Presekal, Alfan Ramli, Kalamullah |
| description | Iris recognition is an alternative authentication method. Many studies have tried to improve iris recognition as a biometric-based alternative for secure authentication. Iris segmentation is an important part of iris recognition because it defines the image region that is used for subsequent processing such as feature extraction and matching, hence directly affects the overall iris recognition performance. This work focuses on the development of an authentication system using localization methods and half-polar normalization of the iris. The proposed Zeta method uses a new model of eye segmentation and normalization that can be used simultaneously on both eyes, considering different iris patterns in those two eyes. There are seven variants of the proposed and tested Zeta method: Zeta-v1, Zeta-v2, Zeta-v3, Zeta-v4, Zeta-v5, Zeta-v6, and Zeta-v7. Overall, the method achieved an average segmentation time performance of 0.0138427 seconds. The most accurate rate was by the Zeta-v1 method, with a value threshold of 100% on the wrong rejection rate and 94.9% on the correct acceptance rate. |
| doi_str_mv | 10.14569/IJACSA.2020.0110742 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2655154091</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2655154091</sourcerecordid><originalsourceid>FETCH-LOGICAL-c274t-6bd79b0972e7d3622b5e73418c1f446ff88371c939b249926805bac631d41bab3</originalsourceid><addsrcrecordid>eNotkFtLAzEQhYMoWGr_gQ8BX92aezaPy3pbqQhqQXwJyW6iW9pNTbZK_73by7zMwJwznPkAuMRoihkX6qZ6Ksq3YkoQQVOEMZKMnIARwVxknEt0up_zbFh8nINJSgs0FFVE5HQE5rfu1y3DeuW6HpqugUVnltvUJhg8NPDT9QY-u_47NNCHCGfhLytD6q9haVYumsya5BpYxcHw6urw1bV9G7oLcObNMrnJsY_B_P7uvXzMZi8PVVnMsppI1mfCNlJZpCRxsqGCEMudpAznNfaMCe_znEpcK6osYWqXGHFrakFxw7A1lo7B1eHuOoafjUu9XoRNHD5ImgjOMWdI4UHFDqo6hpSi83od25WJW42R3jPUB4Z6x1AfGdJ_F9disQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2655154091</pqid></control><display><type>article</type><title>Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Ihsanto, Eko ; Kurniawan, Jeffry ; Husna, Diyanatul ; Presekal, Alfan ; Ramli, Kalamullah</creator><creatorcontrib>Ihsanto, Eko ; Kurniawan, Jeffry ; Husna, Diyanatul ; Presekal, Alfan ; Ramli, Kalamullah</creatorcontrib><description>Iris recognition is an alternative authentication method. Many studies have tried to improve iris recognition as a biometric-based alternative for secure authentication. Iris segmentation is an important part of iris recognition because it defines the image region that is used for subsequent processing such as feature extraction and matching, hence directly affects the overall iris recognition performance. This work focuses on the development of an authentication system using localization methods and half-polar normalization of the iris. The proposed Zeta method uses a new model of eye segmentation and normalization that can be used simultaneously on both eyes, considering different iris patterns in those two eyes. There are seven variants of the proposed and tested Zeta method: Zeta-v1, Zeta-v2, Zeta-v3, Zeta-v4, Zeta-v5, Zeta-v6, and Zeta-v7. Overall, the method achieved an average segmentation time performance of 0.0138427 seconds. The most accurate rate was by the Zeta-v1 method, with a value threshold of 100% on the wrong rejection rate and 94.9% on the correct acceptance rate.</description><identifier>ISSN: 2158-107X</identifier><identifier>EISSN: 2156-5570</identifier><identifier>DOI: 10.14569/IJACSA.2020.0110742</identifier><language>eng</language><publisher>West Yorkshire: Science and Information (SAI) Organization Limited</publisher><subject>Accuracy ; Biometric recognition systems ; Biometrics ; Cameras ; Computer engineering ; Computer science ; Cost analysis ; Feature extraction ; Image segmentation ; Localization ; Methods ; Noise ; Object recognition ; Rejection rate</subject><ispartof>International journal of advanced computer science & applications, 2020, Vol.11 (7)</ispartof><rights>2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids></links><search><creatorcontrib>Ihsanto, Eko</creatorcontrib><creatorcontrib>Kurniawan, Jeffry</creatorcontrib><creatorcontrib>Husna, Diyanatul</creatorcontrib><creatorcontrib>Presekal, Alfan</creatorcontrib><creatorcontrib>Ramli, Kalamullah</creatorcontrib><title>Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition</title><title>International journal of advanced computer science & applications</title><description>Iris recognition is an alternative authentication method. Many studies have tried to improve iris recognition as a biometric-based alternative for secure authentication. Iris segmentation is an important part of iris recognition because it defines the image region that is used for subsequent processing such as feature extraction and matching, hence directly affects the overall iris recognition performance. This work focuses on the development of an authentication system using localization methods and half-polar normalization of the iris. The proposed Zeta method uses a new model of eye segmentation and normalization that can be used simultaneously on both eyes, considering different iris patterns in those two eyes. There are seven variants of the proposed and tested Zeta method: Zeta-v1, Zeta-v2, Zeta-v3, Zeta-v4, Zeta-v5, Zeta-v6, and Zeta-v7. Overall, the method achieved an average segmentation time performance of 0.0138427 seconds. The most accurate rate was by the Zeta-v1 method, with a value threshold of 100% on the wrong rejection rate and 94.9% on the correct acceptance rate.</description><subject>Accuracy</subject><subject>Biometric recognition systems</subject><subject>Biometrics</subject><subject>Cameras</subject><subject>Computer engineering</subject><subject>Computer science</subject><subject>Cost analysis</subject><subject>Feature extraction</subject><subject>Image segmentation</subject><subject>Localization</subject><subject>Methods</subject><subject>Noise</subject><subject>Object recognition</subject><subject>Rejection rate</subject><issn>2158-107X</issn><issn>2156-5570</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNotkFtLAzEQhYMoWGr_gQ8BX92aezaPy3pbqQhqQXwJyW6iW9pNTbZK_73by7zMwJwznPkAuMRoihkX6qZ6Ksq3YkoQQVOEMZKMnIARwVxknEt0up_zbFh8nINJSgs0FFVE5HQE5rfu1y3DeuW6HpqugUVnltvUJhg8NPDT9QY-u_47NNCHCGfhLytD6q9haVYumsya5BpYxcHw6urw1bV9G7oLcObNMrnJsY_B_P7uvXzMZi8PVVnMsppI1mfCNlJZpCRxsqGCEMudpAznNfaMCe_znEpcK6osYWqXGHFrakFxw7A1lo7B1eHuOoafjUu9XoRNHD5ImgjOMWdI4UHFDqo6hpSi83od25WJW42R3jPUB4Z6x1AfGdJ_F9disQ</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Ihsanto, Eko</creator><creator>Kurniawan, Jeffry</creator><creator>Husna, Diyanatul</creator><creator>Presekal, Alfan</creator><creator>Ramli, Kalamullah</creator><general>Science and Information (SAI) Organization Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>2020</creationdate><title>Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition</title><author>Ihsanto, Eko ; Kurniawan, Jeffry ; Husna, Diyanatul ; Presekal, Alfan ; Ramli, Kalamullah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c274t-6bd79b0972e7d3622b5e73418c1f446ff88371c939b249926805bac631d41bab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Biometric recognition systems</topic><topic>Biometrics</topic><topic>Cameras</topic><topic>Computer engineering</topic><topic>Computer science</topic><topic>Cost analysis</topic><topic>Feature extraction</topic><topic>Image segmentation</topic><topic>Localization</topic><topic>Methods</topic><topic>Noise</topic><topic>Object recognition</topic><topic>Rejection rate</topic><toplevel>online_resources</toplevel><creatorcontrib>Ihsanto, Eko</creatorcontrib><creatorcontrib>Kurniawan, Jeffry</creatorcontrib><creatorcontrib>Husna, Diyanatul</creatorcontrib><creatorcontrib>Presekal, Alfan</creatorcontrib><creatorcontrib>Ramli, Kalamullah</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of advanced computer science & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ihsanto, Eko</au><au>Kurniawan, Jeffry</au><au>Husna, Diyanatul</au><au>Presekal, Alfan</au><au>Ramli, Kalamullah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition</atitle><jtitle>International journal of advanced computer science & applications</jtitle><date>2020</date><risdate>2020</risdate><volume>11</volume><issue>7</issue><issn>2158-107X</issn><eissn>2156-5570</eissn><abstract>Iris recognition is an alternative authentication method. Many studies have tried to improve iris recognition as a biometric-based alternative for secure authentication. Iris segmentation is an important part of iris recognition because it defines the image region that is used for subsequent processing such as feature extraction and matching, hence directly affects the overall iris recognition performance. This work focuses on the development of an authentication system using localization methods and half-polar normalization of the iris. The proposed Zeta method uses a new model of eye segmentation and normalization that can be used simultaneously on both eyes, considering different iris patterns in those two eyes. There are seven variants of the proposed and tested Zeta method: Zeta-v1, Zeta-v2, Zeta-v3, Zeta-v4, Zeta-v5, Zeta-v6, and Zeta-v7. Overall, the method achieved an average segmentation time performance of 0.0138427 seconds. The most accurate rate was by the Zeta-v1 method, with a value threshold of 100% on the wrong rejection rate and 94.9% on the correct acceptance rate.</abstract><cop>West Yorkshire</cop><pub>Science and Information (SAI) Organization Limited</pub><doi>10.14569/IJACSA.2020.0110742</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2158-107X |
| ispartof | International journal of advanced computer science & applications, 2020, Vol.11 (7) |
| issn | 2158-107X 2156-5570 |
| language | eng |
| recordid | cdi_proquest_journals_2655154091 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Accuracy Biometric recognition systems Biometrics Cameras Computer engineering Computer science Cost analysis Feature extraction Image segmentation Localization Methods Noise Object recognition Rejection rate |
| title | Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T20%3A27%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20and%20Analysis%20of%20a%20Zeta%20Method%20for%20Low-Cost,%20Camera-based%20Iris%20Recognition&rft.jtitle=International%20journal%20of%20advanced%20computer%20science%20&%20applications&rft.au=Ihsanto,%20Eko&rft.date=2020&rft.volume=11&rft.issue=7&rft.issn=2158-107X&rft.eissn=2156-5570&rft_id=info:doi/10.14569/IJACSA.2020.0110742&rft_dat=%3Cproquest_cross%3E2655154091%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2655154091&rft_id=info:pmid/&rfr_iscdi=true |