Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming
Identifieur interne : 001078 ( Main/Exploration ); précédent : 001077; suivant : 001079Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming
Auteurs : Wannasak Wetcharaporn [Thaïlande] ; Nachol Chaiyaratana [Thaïlande] ; Sanpachai Huvanandana [Thaïlande]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: This paper presents the use of a genetic algorithm and genetic programming for the enhancement of an automatic fingerprint identification system (AFIS). The recognition engine within the original system functions by transforming the input fingerprint into a feature vector or fingercode using a Gabor filter bank and attempting to create the best match between the input fingercode and the database fingercodes. A decision to either accept or reject the input fingerprint is then carried out based upon whether the norm of the difference between the input fingercode and the best-matching database fingercode is within the threshold or not. The efficacy of the system is in general determined from the combined true acceptance and true rejection rates. In this investigation, a genetic algorithm is applied during the pruning of the fingercode while the search by genetic programming is executed for the purpose of creating a mathematical function that can be used as an alternative to the norm operator. The results indicate that with the use of both genetic algorithm and genetic programming the system performance has improved significantly.
Url:
DOI: 10.1007/11732242_33
Affiliations:
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The recognition engine within the original system functions by transforming the input fingerprint into a feature vector or fingercode using a Gabor filter bank and attempting to create the best match between the input fingercode and the database fingercodes. A decision to either accept or reject the input fingerprint is then carried out based upon whether the norm of the difference between the input fingercode and the best-matching database fingercode is within the threshold or not. The efficacy of the system is in general determined from the combined true acceptance and true rejection rates. In this investigation, a genetic algorithm is applied during the pruning of the fingercode while the search by genetic programming is executed for the purpose of creating a mathematical function that can be used as an alternative to the norm operator. The results indicate that with the use of both genetic algorithm and genetic programming the system performance has improved significantly.</div></front></TEI><affiliations><list><country><li>Thaïlande</li></country></list><tree><country name="Thaïlande"><noRegion><name sortKey="Wetcharaporn, Wannasak" sort="Wetcharaporn, Wannasak" uniqKey="Wetcharaporn W" first="Wannasak" last="Wetcharaporn">Wannasak Wetcharaporn</name></noRegion><name sortKey="Chaiyaratana, Nachol" sort="Chaiyaratana, Nachol" uniqKey="Chaiyaratana N" first="Nachol" last="Chaiyaratana">Nachol Chaiyaratana</name><name sortKey="Chaiyaratana, Nachol" sort="Chaiyaratana, Nachol" uniqKey="Chaiyaratana N" first="Nachol" last="Chaiyaratana">Nachol Chaiyaratana</name><name sortKey="Huvanandana, Sanpachai" sort="Huvanandana, Sanpachai" uniqKey="Huvanandana S" first="Sanpachai" last="Huvanandana">Sanpachai Huvanandana</name><name sortKey="Huvanandana, Sanpachai" sort="Huvanandana, Sanpachai" uniqKey="Huvanandana S" first="Sanpachai" last="Huvanandana">Sanpachai Huvanandana</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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