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Cancelable biometrics realization with multispace random projections.

Andrew Beng Jin Teoh1, Chong Tze Yuang

  • 1Biometrics Engineering Research Center (BERC), Yonsei University, Seoul 120-749, Korea. bjteoh@ieee.org

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 12, 2007
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Biometric data, once compromised, leads to permanent privacy loss. This study introduces a two-factor cancelable biometric system that distorts data revocably, allowing easy replacement of compromised biometric information.

Area of Science:

  • Computer Science
  • Information Security
  • Biometrics

Background:

  • Biometric data, once compromised, results in irreversible privacy loss due to its inherent permanence.
  • Existing biometric systems lack robust mechanisms for revoking and reissuing compromised credentials.

Purpose of the Study:

  • To present a novel two-factor cancelable biometric formulation.
  • To ensure revocable yet non-reversible distortion of biometric data.
  • To enhance biometric security by enabling easy replacement of compromised credentials.

Main Methods:

  • Transforming raw biometric data into a fixed-length feature vector.
  • Projecting the feature vector onto random subspaces derived from user-specific pseudorandom numbers (PRNs).
  • Evaluating the formulation using 2400 Facial Recognition Technology (FRT) face images across various scenarios.

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Main Results:

  • The proposed two-factor formulation offers revocable distortion of biometric data.
  • The system demonstrated effectiveness in normal, stolen PRN, and compromised biometric scenarios.
  • Analysis of the diversity property of the cancelable biometric system was performed.

Conclusions:

  • The developed cancelable biometric system provides a secure and flexible solution for managing biometric data.
  • The revocable nature of the system allows for efficient replacement of compromised biometrics, akin to replacing PRNs.
  • This approach mitigates the permanent privacy risks associated with traditional biometric systems.