An Application for Singular Point Location in Fingerprint Classification

  • Ali Ismail Awad
  • Kensuke Baba
Part of the Communications in Computer and Information Science book series (CCIS, volume 188)

Abstract

Singular Point (SP) is one of the local fingerprint features, and it is used as a landmark due its scale and rotation immutability. SP characteristics have been widely used as a feature vector for many fingerprint classification approaches. This paper introduces a new application of singular point location in fingerprint classification by considering it as a reference point to the partitioning process in the proposed pattern-based classification algorithm. The key idea of the proposed classification method is dividing fingerprint into small sub images using SP location, and then, creating distinguished patterns for each class using frequency domain representation for each sub-image. The performance evaluation of the SP detection and the proposed algorithm with different database sub-sets focused on both the processing time and the classification accuracy as key issues of any classification approach. The experimental work shows the superiority of using singular point location with the proposed classification algorithm. The achieved classification accuracy over FVC2002 database subsets is up to 91.4% with considerable processing time and robustness to scale, shift, and rotation conditions.

Keywords

Fingerprint Classification Singular Point Texture Patterns 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ali Ismail Awad
    • 1
  • Kensuke Baba
    • 2
  1. 1.Graduate School of Information Science and Electrical EngineeringKyushu UniversityJapan
  2. 2.Research and Development DivisionKyushu University LibraryJapan

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