Detecting Liveness in Fingerprint Scanners Using Wavelets: Results of the Test Dataset

  • Stephanie Schuckers
  • Aditya Abhyankar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3087)


A novel method is proposed to detect “liveness” associated with fingerprint devices. The physiological phenomenon of perspiration, observed only in live people, is used as a measure to classify ‘live’ fingers from ‘not live’ fingers. Pre-processing involves filtering of the images using different image processing techniques. Wavelet analysis of the images is performed using Daubechies wavelet. Multiresolution analysis is performed to extract information from the low frequency content, while wavelet packet analysis is performed to analyze the high frequency information content. A threshold is applied to the first difference of the information in all the sub-bands. The energy content of the changing wavelet coefficients, which are directly associated with the perspiration pattern, is used as a quantified measure to differentiate live fingers from others. The proposed algorithm was applied to a data set of approximately 30 live, 30 spoof and 14 cadaver fingerprint images from three different types of scanners. The algorithm was able to completely classify ‘live’ fingers from ‘not live’ fingers providing a method for improved spoof protection.


Wavelet Packet Multiresolution Analysis Daubechies Wavelet Wavelet Packet Analysis Base Wavelet Packet 
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 2004

Authors and Affiliations

  • Stephanie Schuckers
    • 1
    • 2
  • Aditya Abhyankar
    • 1
  1. 1.Department of Electrical and Computer EngineeringClarkson UniversityPotsdamUSA
  2. 2.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

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