Computational Algorithms for Fingerprint Recognition
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- @PhdThesis{Xuejun_Tan:thesis,
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author = "Xuejun Tan",
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title = "Computational Algorithms for Fingerprint Recognition",
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school = "Electrical Engineering, University of California,
Riverside",
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year = "2003",
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address = "USA",
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month = jun,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://search.proquest.com/docview/305355283",
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size = "200 pages",
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abstract = "Biometrics, which recognizes a person's identity using
his/her physiological or behavioural characteristics,
is inherently more reliable and capable than
traditional methods. Biometric signs include
fingerprint, face, gait, iris, voice, signature, etc.
Among them, fingerprint is the one, which has been
researched for a long time and shows the most promising
future in real-world applications. However, because of
the complex distortions among the different impressions
of the same finger, fingerprint recognition is still a
challenging problem. In this dissertation, our
objective is to develop effective and efficient
computational algorithms for an automatic fingerprint
recognition system. The algorithms we address include:
(1) Templates based minutiae extraction algorithm; (2)
Triplets of minutiae based fingerprint indexing
algorithm; (3) Genetic Algorithm based fingerprint
matching algorithm; (4) Genetic Programming based
feature learning algorithm for fingerprint
classification; (5) Comparison of classification and
indexing in identification; and (6) Fundamental
performance analysis of fingerprint matching. All the
experimental results are demonstrated on standard
fingerprint database, NIST-4 fingerprint database.
Although the algorithms we have developed can achieve a
good performance in fingerprint recognition, we believe
that there are still some problems need to be worked on
to make automatic fingerprint recognition system more
effective and efficient in real-world applications. We
believe that it needs incorporation of researchers from
different fields, such as Computer Science, Electrical
Engineering, Physiology, Statistics, Social Sciences,
etc. So that, it is possible to achieve a better
fingerprint recognition performance, which is close to
theoretical bound.",
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notes = "Supervisor: Bir Bhanu
UMI Microform 3096780",
- }
Genetic Programming entries for
Xuejun Tan
Citations