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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Main Authors: Turky A.M., Ahmad M.S.
Other Authors: 25825717300
Format: Conference paper
Published: 2023
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spelling 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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic 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
spellingShingle 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
description 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.
author2 25825717300
author_facet 25825717300
Turky A.M.
Ahmad M.S.
format Conference paper
author Turky A.M.
Ahmad M.S.
author_sort 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
title_full_unstemmed The use of SOM for fingerprint classification
title_sort use of som for fingerprint classification
publishDate 2023
_version_ 1806428037938413568
score 13.214268