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Fingerprint matching based on global comprehensive similarity.

Yuliang He1, Jie Tian, Liang Li

  • 1Center for Biometrics and Security Research, Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Science, PO Box 2728, Beijing 100080, China. yuliang.he@ia.ac.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 27, 2006
PubMed
Summary

This study presents a new fingerprint recognition algorithm using comprehensive similarity. The novel method achieves high accuracy and is suitable for limited memory Automated Fingerprint Identification Systems (AFIS) with small template sizes.

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Area of Science:

  • Biometrics
  • Computer Science
  • Pattern Recognition

Background:

  • Fingerprint recognition is crucial for security.
  • Existing methods face challenges with template size and computational efficiency.

Purpose of the Study:

  • To introduce a novel algorithm for fingerprint recognition.
  • To develop a method suitable for memory-constrained Automated Fingerprint Identification Systems (AFIS).

Main Methods:

  • A three-step algorithm based on global comprehensive similarity.
  • Utilizing Euclidean space-based relative features via minutia-simplex.
  • Incorporating ridge-based nearest neighborhood features for minutiae grouping.
  • Modeling transformation-similarity relationship using histograms for parameter estimation.

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Main Results:

  • The algorithm effectively uses both Euclidean and ridge-based features.
  • Achieved a template size of less than 1 kilobyte.
  • Demonstrated effectiveness and suitability for limited memory AFIS.

Conclusions:

  • The proposed algorithm offers an efficient solution for fingerprint matching.
  • The method is practical for AFIS applications with memory limitations.
  • The comprehensive similarity approach enhances recognition accuracy.