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Fingerprint pattern classification approach based on the coordinate geometry of singularities

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dc.contributor.author Msiza, IS
dc.contributor.author Leke-Betechuoh, B
dc.contributor.author Nelwamondo, Fulufhelo V
dc.contributor.author Msimang, N
dc.date.accessioned 2009-11-20T13:27:48Z
dc.date.available 2009-11-20T13:27:48Z
dc.date.issued 2009-10
dc.identifier.citation Msiza, IS, Leke-Betechuoh, B, Nelwamondo, FV and Msimang, N. 2009. Fingerprint pattern classification approach based on the coordinate geometry of singularities. 2009 IEEE International Conference on Systems, Man, and Cybernetics. San Antonio, Texas, USA, 11 - 14 October 2009, pp 516-523 en
dc.identifier.isbn 9781424427949
dc.identifier.uri http://hdl.handle.net/10204/3769
dc.description Copyright: 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE en
dc.description.abstract The problem of Automatic Fingerprint Pattern Classification (AFPC) has been studied by many fingerprint biometric practitioners. It is an important concept because, in instances where a relatively large database is being queried for the purposes of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed in this problem involve the use of the coordinate geometry of the detected singular points. Using a confusion matrix to evaluate the performance of the fingerprint classifier, a classification accuracy of 83.5% was obtained on the five-class problem. This performance evaluation was done by making use of fingerprint images from one of the databases of the year 2002 version of the Fingerprint Verification Competition (FVC2002). en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineering (IEEE) en
dc.subject Automatic fingerprint pattern classification en
dc.subject Biometrics en
dc.subject Coordinate geometry en
dc.subject Central twins en
dc.subject Tented arch en
dc.subject Left loop en
dc.subject Right loop en
dc.subject Plain arch
dc.title Fingerprint pattern classification approach based on the coordinate geometry of singularities en
dc.type Conference Presentation en
dc.identifier.apacitation Msiza, I., Leke-Betechuoh, B., Nelwamondo, F. V., & Msimang, N. (2009). Fingerprint pattern classification approach based on the coordinate geometry of singularities. Institute of Electrical and Electronics Engineering (IEEE). http://hdl.handle.net/10204/3769 en_ZA
dc.identifier.chicagocitation Msiza, IS, B Leke-Betechuoh, Fulufhelo V Nelwamondo, and N Msimang. "Fingerprint pattern classification approach based on the coordinate geometry of singularities." (2009): http://hdl.handle.net/10204/3769 en_ZA
dc.identifier.vancouvercitation Msiza I, Leke-Betechuoh B, Nelwamondo FV, Msimang N, Fingerprint pattern classification approach based on the coordinate geometry of singularities; Institute of Electrical and Electronics Engineering (IEEE); 2009. http://hdl.handle.net/10204/3769 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Msiza, IS AU - Leke-Betechuoh, B AU - Nelwamondo, Fulufhelo V AU - Msimang, N AB - The problem of Automatic Fingerprint Pattern Classification (AFPC) has been studied by many fingerprint biometric practitioners. It is an important concept because, in instances where a relatively large database is being queried for the purposes of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed in this problem involve the use of the coordinate geometry of the detected singular points. Using a confusion matrix to evaluate the performance of the fingerprint classifier, a classification accuracy of 83.5% was obtained on the five-class problem. This performance evaluation was done by making use of fingerprint images from one of the databases of the year 2002 version of the Fingerprint Verification Competition (FVC2002). DA - 2009-10 DB - ResearchSpace DP - CSIR KW - Automatic fingerprint pattern classification KW - Biometrics KW - Coordinate geometry KW - Central twins KW - Tented arch KW - Left loop KW - Right loop KW - Plain arch LK - https://researchspace.csir.co.za PY - 2009 SM - 9781424427949 T1 - Fingerprint pattern classification approach based on the coordinate geometry of singularities TI - Fingerprint pattern classification approach based on the coordinate geometry of singularities UR - http://hdl.handle.net/10204/3769 ER - en_ZA


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