Digital Information Processing and Communications

Volume 188 of the series Communications in Computer and Information Science pp 262-276

An Application for Singular Point Location in Fingerprint Classification

  • Ali Ismail AwadAffiliated withGraduate School of Information Science and Electrical Engineering, Kyushu University
  • , Kensuke BabaAffiliated withResearch and Development Division, Kyushu University Library

* Final gross prices may vary according to local VAT.

Get Access


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.


Fingerprint Classification Singular Point Texture Patterns