An analysis of BioHashing and its variants

  • Authors:

  • Adams Kong;King-Hong Cheung;David Zhang;Mohamed Kamel;Jane You

  • Affiliations:

  • Department of Computing, Biometrics Research Centre, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong and Pattern Analysis and Machine Intelligence Lab, University of Waterloo, 2 ...;Department of Computing, Biometrics Research Centre, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Department of Computing, Biometrics Research Centre, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Pattern Analysis and Machine Intelligence Lab, University of Waterloo, 200 University Avenue West, Ontario, Canada;Department of Computing, Biometrics Research Centre, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

  • Venue:
  • Pattern Recognition
  • Year:
  • 2006

Quantified Score

Hi-index 0.01

Visualization

Abstract

As a result of the growing demand for accurate and reliable personal authentication, biometric recognition, a substitute for or complement to existing authentication technologies, has attracted considerable attention. It has recently been reported that, along with its variants, BioHashing, a new technique that combines biometric features and a tokenized (pseudo-) random number (TRN), has achieved perfect accuracy, having zero equal error rates (EER) for faces, fingerprints and palmprints. There are, however, anomalies in this approach. These are identified in this paper, in which we systematically analyze the details of the approach and conclude that the claim of having achieved a zero EER is based upon an impractical hidden assumption. We simulate the claimants' experiments and find that it is not possible to achieve their reported performance without the hidden assumption and that, indeed, the results are worse than when using the biometric alone.