Computational Algorithms for Fingerprint Recognition

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  author =       "Xuejun Tan",
  title =        "Computational Algorithms for Fingerprint Recognition",
  school =       "Electrical Engineering, University of California,
  year =         "2003",
  address =      "USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "200 pages",
  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.",
  notes =        "Supervisor: Bir Bhanu

                 UMI Microform 3096780",

Genetic Programming entries for Xuejun Tan