Fingerprint Quality Indices for Predicting Authentication Performance

  • Yi Chen
  • Sarat C. Dass
  • Anil K. Jain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)


The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.


Quality Index Authentication System Fingerprint Image Matching Performance Enhancement Algorithm 
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 2005

Authors and Affiliations

  • Yi Chen
    • 1
  • Sarat C. Dass
    • 2
  • Anil K. Jain
    • 1
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast Lansing
  2. 2.Department of StatisticsMichigan State UniversityEast Lansing

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