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On random transformations for changeable face verification.

Yongjin Wang1, Dimitrios Hatzinakos

  • 1Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada. ywang@ee.ryerson.ca

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 4, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces random transformations to create changeable and privacy-preserving biometric templates. These methods enhance biometric verification systems by generating secure, non-invertible templates from facial data.

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Area of Science:

  • Computer Science
  • Information Security
  • Biometrics

Background:

  • Biometric technology deployment requires changeable and privacy-preserving templates.
  • Existing systems face challenges in template security and adaptability.

Purpose of the Study:

  • To systematically analyze random transformation-based methods for biometric template security.
  • To address changeability and privacy concerns in biometrics-based verification systems.

Main Methods:

  • Transforming original biometric feature vectors using random transformations.
  • Storing sorted index numbers (SIN) of transformed vectors as biometric templates.
  • Analyzing random additive transform, random multiplicative transform, and random projection.

Main Results:

  • Random transformations combined with SIN create repeatable and noninvertible transformations.
  • Generated templates are changeable and offer robust privacy protection.
  • Effectiveness demonstrated through analysis and extensive experimentation on face verification.

Conclusions:

  • Random transformation-based methods offer a viable solution for secure and adaptable biometric templates.
  • The proposed SIN approach enhances privacy and changeability in biometric systems.
  • The methods are effective for real-world applications like face verification.