The use of SOM for fingerprint classification
The use of efficient classification methods is necessary for automatic fingerprint recognition systems. This paper introduces an approach to fingerprint classification by using Self-Organizing Maps (SOM). In order to be able to deal with fingerprint images having distorted regions, the SOM learning...
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my.uniten.dspace-307062023-12-29T15:51:37Z The use of SOM for fingerprint classification Turky A.M. Ahmad M.S. 25825717300 56036880900 Biometric Fingerprint classification Self organizing maps Biometrics Conformal mapping Image recognition Information retrieval Knowledge management Learning algorithms Automatic fingerprint recognition system Classification algorithm Classification methods Fingerprint classification Fingerprint database Fingerprint images Indexing mechanisms Modified algorithms Network size Self organizing maps The use of efficient classification methods is necessary for automatic fingerprint recognition systems. This paper introduces an approach to fingerprint classification by using Self-Organizing Maps (SOM). In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. The concept of 'certainty' is introduced and used in the modified algorithms. Our experiments show improved results with increasing network sizes. A network that is trained with a sufficiently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retrained for each fingerprint added to the database. �2010 IEEE. Final 2023-12-29T07:51:37Z 2023-12-29T07:51:37Z 2010 Conference paper 10.1109/INFRKM.2010.5466901 2-s2.0-77953879535 https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953879535&doi=10.1109%2fINFRKM.2010.5466901&partnerID=40&md5=558ea1c41c722f35fcc141b92f3c0859 https://irepository.uniten.edu.my/handle/123456789/30706 5466901 287 290 Scopus |
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Biometric Fingerprint classification Self organizing maps Biometrics Conformal mapping Image recognition Information retrieval Knowledge management Learning algorithms Automatic fingerprint recognition system Classification algorithm Classification methods Fingerprint classification Fingerprint database Fingerprint images Indexing mechanisms Modified algorithms Network size Self organizing maps |
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Biometric Fingerprint classification Self organizing maps Biometrics Conformal mapping Image recognition Information retrieval Knowledge management Learning algorithms Automatic fingerprint recognition system Classification algorithm Classification methods Fingerprint classification Fingerprint database Fingerprint images Indexing mechanisms Modified algorithms Network size Self organizing maps Turky A.M. Ahmad M.S. The use of SOM for fingerprint classification |
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The use of efficient classification methods is necessary for automatic fingerprint recognition systems. This paper introduces an approach to fingerprint classification by using Self-Organizing Maps (SOM). In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. The concept of 'certainty' is introduced and used in the modified algorithms. Our experiments show improved results with increasing network sizes. A network that is trained with a sufficiently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retrained for each fingerprint added to the database. �2010 IEEE. |
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25825717300 |
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25825717300 Turky A.M. Ahmad M.S. |
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Conference paper |
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Turky A.M. Ahmad M.S. |
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Turky A.M. |
title |
The use of SOM for fingerprint classification |
title_short |
The use of SOM for fingerprint classification |
title_full |
The use of SOM for fingerprint classification |
title_fullStr |
The use of SOM for fingerprint classification |
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The use of SOM for fingerprint classification |
title_sort |
use of som for fingerprint classification |
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2023 |
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1806428037938413568 |
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13.214268 |