TR2008-029

Feature Transformation of Biometric Templates for Secure Biometric Systems Based on Error Correcting Codes


    •  Sutcu, Y., Rane, S., Yedidia, J.S., Draper, S.C., Vetro, A., "Feature Transformation of Biometric Templates for Secure Biometric Systems based on Error Correcting Codes", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2008.
      BibTeX TR2008-029 PDF
      • @inproceedings{Sutcu2008jun,
      • author = {Sutcu, Y. and Rane, S. and Yedidia, J.S. and Draper, S.C. and Vetro, A.},
      • title = {Feature Transformation of Biometric Templates for Secure Biometric Systems based on Error Correcting Codes},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2008,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2008-029}
      • }
  • Research Area:

    Information Security

TR Image
Robust feature extraction is combined with syndrome coding to build a secure fingerprint biometrics system.

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.

 

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      Date: June 28, 2008
      Where: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
      MERL Contacts: Matthew Brand; Jay Thornton; Anthony Vetro
      Brief
      • The papers "Non-Refractive Modulators for Encoding and Capturing Scene Appearance and Depth" by Veeraraghavan, A., Agrawal, A., Raskar, R., Mohan, A. and Tumblin, J., "Feature Transformation of Biometric Templates for Secure Biometric Systems based on Error Correcting Codes" by Sutcu, Y., Rane, S., Yedidia, J.S., Draper, S.C. and Vetro, A., "Constant Time O(1) Bilateral Filtering" by Porikli, F., "Learning on Lie Groups for Invariant Detection and Tracking" by Tuzel, O., Porikli, F. and Meer, P., "Kernel Integral Images: A Framework for Fast non-Uniform Filtering" by Hussein, M., Porikli, F. and Davis, L., "A Conditional Random Field for Automatic Photo Editing" by Brand, M. and Pletscher, P., "Boosting Adaptive Linear Weak Classifiers for Online Learning and Tracking" by Parag, T., Porikli, F. and Elgammai, A. and "Sensing Increased Image Resolution Using Aperture Masks" by Mohan, A., Huang, X., Tumblin, J. and Raskar, R. were presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
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