A Novel Region Based Liveness Detection Approach for Fingerprint Scanners

  • Brian DeCann
  • Bozhao Tan
  • Stephanie Schuckers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Biometric scanners have become widely popular in providing security to information technology and entry to otherwise sensitive locations. However, these systems have been proven to be vulnerable to spoofing, or granting entry to an imposter using fake fingers. While matching algorithms are highly successful in identifying the unique fingerprint biometric of an individual, they lack the ability to determine if the source of the image is coming from a living individual, or a fake finger, comprised of PlayDoh, silicon, gelatin or other material. Detection of liveness patterns is one method in which physiological traits are identified in order to ensure that the image received by the scanner is coming from a living source. In this paper, a new algorithm for detection of perspiration is proposed. The method quantifies perspiration via region labeling methods, a simple computer vision technique. This method is capable of extracting observable trends in live and spoof images, generally relating to the differences found in the number and size of identifiable regions per contour along a ridge or valley segment. This approach was tested on a optical fingerprint scanner, Identix DFR2100. The dataset includes a total of 1526 live and 1588 spoof fingerprints, arising from over 150 unique individuals with multiple visits. Performance was evaluated through a neural network classifier, and the results are compared to previous studies using intensity based ridge and valley liveness detection. The results yield excellent classification, achieving overall classification rates greater than 95.5%. Implementation of this liveness detection method can greatly improve the security of fingerprint scanners.


Biometrics fingerprint liveness detection neural network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Brian DeCann
    • 1
  • Bozhao Tan
    • 1
  • Stephanie Schuckers
    • 1
  1. 1.Clarkson UniversityPotsdamUSA

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