Active Fingerprint Ridge Orientation Models

  • Surinder Ram
  • Horst Bischof
  • Josef Birchbauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

This paper proposes a statistical model for fingerprint ridge orientations. The active fingerprint ridge orientation model (AFROM) iteratively deforms to fit the orientation field (OF) of a fingerprint. The OFs are constrained by the AFROM to vary only in ways according to a training set. The main application of the method is the OF estimation in noisy fingerprints as well as the interpolation and extrapolation of larger OF parts. Fingerprint OFs are represented by Legendre Polynomials. The method does not depend on any pre-alignment or registration of the input image itself. The training can be done fully automatic without any user interaction. We show that the model is able to extract the significant appearance elements of fingerprint flow patterns even from noisy training images. Furthermore, our method does not depend on any other computed data, except a segmentation. We evaluated both, the generalisation as well as the prediction capability of the proposed method. These evaluations assess our method very good results.

Keywords

Orientation Field Foreground Pixel Orientation Estimation Eigenvalue Spectrum Ridge Orientation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Surinder Ram
    • 1
  • Horst Bischof
    • 1
  • Josef Birchbauer
    • 2
  1. 1.Institute for Computer Graphics and VisionUniversity of Technology GrazAustria
  2. 2.Siemens Austria, Siemens IT Solutions and Services Biometrics CenterAustria

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