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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Feature transformation of biometric templates for secure biometric systems based on error correcting codes
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Yagiz Sutcu, Polytechnic University, Brooklyn, NY 11201, USA
Shantanu Rane, Mitsubishi Electric Research Labs, Cambridge, MA 02139, USA
Jonathan S. Yedidia, Mitsubishi Electric Research Labs, Cambridge, MA 02139, USA
Stark C. Draper, University of Wisconsin, Madison, 53706, USA
Anthony Vetro, Mitsubishi Electric Research Labs, Cambridge, MA 02139, USA
Secure storage of biometric templates is extremely important because a compromised biometric cannot be revoked and replaced an unlimited number of times. In many approaches proposed for secure biometric storage, an error correcting code (ECC) is applied to the enrollment biometric and the resulting parity or syndrome symbols are stored on the access control device, instead of the original biometric. The principal challenge here is that most standard ECCs are designed for memoryless channel statistics, whereas the variations between enrollment and probe biometrics have significant spatial correlation. To address this challenge, we propose to transform the original biometric into a feature vector that is explicitly matched to standard ECCs, thereby improving the security-robustness tradeoff of the overall biometric system. As a concrete example, we transform fingerprint minutiae maps into feature vectors compatible with ECCs designed for a binary symmetric channel. We conduct a statistical analysis of these feature vectors and show how our feature transformation algorithm may be combined with Low-Density Parity Check (LDPC) codes to obtain a secure fingerprint biometric system.
Citation:
Yagiz Sutcu, Shantanu Rane, Jonathan S. Yedidia, Stark C. Draper, Anthony Vetro, "Feature transformation of biometric templates for secure biometric systems based on error correcting codes," cvprw, pp.1-6, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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