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Robust Point-Based Feature Fingerprint Segmentation Algorithm

  • Chaohong Wu
  • Sergey Tulyakov
  • Venu Govindaraju
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

A critical step in automatic fingerprint recognition is the accurate segmentation of fingerprint images. The objective of fingerprint segmentation is to decide which part of the images belongs to the foreground containing features for recognition and identification, and which part to the background with the noisy area around the boundary of the image. Unsupervised algorithms extract blockwise features. Supervised method usually first extracts point features like coherence, average gray level, variance and Gabor response, then a Fisher linear classifier is chosen for classification. This method provides accurate results, but its computational complexity is higher than most of unsupervised methods. This paper proposes using Harris corner point features to discriminate foreground and background. Shifting a window in any direction around the corner should give a large change in intensity. We observed that the strength of Harris point in the foreground area is much higher than that of Harris point in background area. The underlying mechanism for this segmentation method is that boundary ridge endings are inherently stronger Harris corner points. Some Harris points in noisy blobs might have higher strength, but it can be filtered as outliers using corresponding Gabor response. The experimental results proved the efficiency and accuracy of new method are markedly higher than those of previously described methods.

Keywords

Corner Point Interest Point Fingerprint Image Gabor Feature Harris Corner 
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 2007

Authors and Affiliations

  • Chaohong Wu
    • 1
  • Sergey Tulyakov
    • 1
  • Venu Govindaraju
    • 1
  1. 1.Center for Unified Biometrics and Sensors (CUBS), SUNY at BuffaloUSA

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