Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

A Novel Two-Stage Approach in Rectifying BioHash's Problem under Stolen Token Scenario

  • Lim, Meng-Hui (School of Electrical and Electronic Engineering, College of Engineering, Yonsei University) ;
  • Jeong, Min-Yi (School of Electrical and Electronic Engineering, College of Engineering, Yonsei University) ;
  • Teoh, Andrew Beng Jin (School of Electrical and Electronic Engineering, College of Engineering, Yonsei University)
  • Received : 2010.03.05
  • Accepted : 2010.04.05
  • Published : 2010.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Over recent years, much research attention has been devoted to a two-factor authentication mechanism which integrates both tokenized pseudorandom numbers with user specific biometric features for biometric verification, known as Biohash. The main advantage of Biohash over sole biometrics is that Biohash is able to achieve a zero equal error rate and provide a clean separation of the genuine and imposter populations, thereby allowing elimination of false accept rates without imperiling the false reject rates. Nonetheless, when the token of a user is compromised, the recognition performance of a biometric system drops drastically. As such, a few solutions have been proposed to improve the degraded performance but such improvements appear to be insignificant. In this paper, we investigate and pinpoint the basis of such deterioration. Subsequently, we propose a two-level approach by utilizing strong inner products and fuzzy logic weighting strategies accordingly to increase the original performance of Biohash under this scenario.

Keywords

References

  1. R.M. Bolle, J.H. Connel and N.K. Ratha, "Biometric Perils and Patches," Pattern Recognition 35 (2002) pp. 2727-2738. https://doi.org/10.1016/S0031-3203(01)00247-3
  2. A. Kong, K.-H. Cheung, D. Zhang, M. Kamel and J. You, "An Analysis of Biohashing and its variants," Pattern Recognition, vol. 39, pp. 1359-1368, 2006. https://doi.org/10.1016/j.patcog.2005.10.025
  3. A. Lumini and L. Nanni, "An Improved Biohashing for Human Authentication," Pattern Recognition, vol. 40, pp. 1057-1065, 2007. https://doi.org/10.1016/j.patcog.2006.05.030
  4. F. Samaria and A. Harter, "Parametrisation of a Stochastic Model for Human Face Identification," 2nd IEEE Workshop on Applications of Computer Vision, pp.138-142, 1994.
  5. A.B.J. Teoh, D.C.L Ngo and A. Goh, "Biohashing: Two Factor Authentication Featuring Fingerprint Data and Tokenized Random Number," Pattern Recognition, vol. 37, pp. 2245-2255, 2004. https://doi.org/10.1016/j.patcog.2004.04.011
  6. A.B.J. Teoh, A. Goh and D.C.L. Ngo, "Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs," IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, 2006.
  7. A.B.J. Teoh and C.T. Yuang, "Cancelable Biometrics Realization with Multispace Random Projections," IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 37, no. 5, 2007.
  8. M. Turk and A. Pentland, "Eigenfaces for Recognition, Journal of Cognitive," Neuroscience, vol. 3, no. 1, pp. 71-86, 1991. https://doi.org/10.1162/jocn.1991.3.1.71

Cited by

  1. 얼굴과 사용자 입력정보를 이용하여 안전한 키를 생성하는 방법 vol.27, pp.5, 2010, https://doi.org/10.13089/jkiisc.2017.27.5.1059