Skip to content

Advertisement

  • Research Article
  • Open Access

Biometric Quantization through Detection Rate Optimized Bit Allocation

  • 1Email author,
  • 1,
  • 2 and
  • 2
EURASIP Journal on Advances in Signal Processing20092009:784834

https://doi.org/10.1155/2009/784834

  • Received: 23 January 2009
  • Accepted: 8 April 2009
  • Published:

Abstract

Extracting binary strings from real-valued biometric templates is a fundamental step in many biometric template protection systems, such as fuzzy commitment, fuzzy extractor, secure sketch, and helper data systems. Previous work has been focusing on the design of optimal quantization and coding for each single feature component, yet the binary string—concatenation of all coded feature components—is not optimal. In this paper, we present a detection rate optimized bit allocation (DROBA) principle, which assigns more bits to discriminative features and fewer bits to nondiscriminative features. We further propose a dynamic programming (DP) approach and a greedy search (GS) approach to achieve DROBA. Experiments of DROBA on the FVC2000 fingerprint database and the FRGC face database show good performances. As a universal method, DROBA is applicable to arbitrary biometric modalities, such as fingerprint texture, iris, signature, and face. DROBA will bring significant benefits not only to the template protection systems but also to the systems with fast matching requirements or constrained storage capability.

Keywords

  • Detection Rate
  • Rate Optimize
  • Binary String
  • Discriminative Feature
  • Face Database

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Signals and Systems Group, Faculty of Electrical Engineering, University of Twente, P. O. Box 217, 7500 AE Enschede, The Netherlands
(2)
Philips Research, High Tech Campus, 5656 AE Eindhoven, The Netherlands

Copyright

© C. Chen et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement