Fuzzy key binding strategies based on quantization index modulation (QIM) for biometric encryption (BE) applications

  • Authors:
  • Francis Minhthang Bui;Karl Martin;Haiping Lu;Konstantinos N. Plataniotis;Dimitrios Hatzinakos

  • Affiliations:
  • Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, ON, Canada;Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, ON, Canada;Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore and Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto ...;Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, ON, Canada;Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, ON, Canada

  • Venue:
  • IEEE Transactions on Information Forensics and Security
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Biometric encryption (BE) has recently been identified as a promising paradigm to deliver security and privacy, with unique technical merits and encouraging social implications. An integral component in BE is a key binding method, which is the process of securely combining a signal, containing sensitive information to be protected (i.e., the key), with another signal derived from physiological features (i.e., the biometric). A challenge to this approach is the high degree of noise and variability present in physiological signals. As such, fuzzy methods are needed to enable proper operations, with adequate performance results in terms of false acceptance rate and false rejection rate. In this work, the focus will be on a class of fuzzy key binding methods based on dirty paper coding known as quantization index modulation. While the methods presented are applicable to a wide range of biometric modalities, the face biometric is selected for illustrative purposes, in evaluating the QIM-based solutions for BE systems. Performance evaluation of the investigated methods is reported using data from the CMU PIE face database.