Non-reference Image Quality Assessment for Fingervein Presentation Attack Detection

  • Amrit Pal Singh Bhogal
  • Dominik Söllinger
  • Pauline Trung
  • Jutta Hämmerle-Uhl
  • Andreas UhlEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10269)


Non-reference image quality measures are used to distinguish real biometric data from data as used in presentation/sensor spoofing attacks. An experimental study shows that based on a set of 6 such measures, classification of real vs. fake fingervein data is feasible with an accuracy of 99% on one of our datasets. However, we have found that the best quality measure (combination) and classification setting highly depends on the target dataset. Thus, we are unable to provide any other recommendation than to optimise the choice of quality measure and classification setting for each specific application setting. Results also imply, that generalisation to unseen attack types might be difficult due to dataset dependence of the results.


Discrete Cosine Transform Local Binary Pattern Discrete Cosine Transform Coefficient Image Quality Assessment Generalise Gaussian Distribution 
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.



This work has been partially supported by the Austrian Science Fund, project no. 27776.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Amrit Pal Singh Bhogal
    • 1
  • Dominik Söllinger
    • 1
  • Pauline Trung
    • 1
  • Jutta Hämmerle-Uhl
    • 1
  • Andreas Uhl
    • 1
    Email author
  1. 1.Visual Computing and Security Lab (VISEL), Department of Computer SciencesUniversity of SalzburgSalzburgAustria

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