A Hybrid Template Protection Approach using
Secure Sketch and ANN for Strong Biometric Key Generation with Revocability
Guarantee
Tran-Khanh Dang1,2,
Van-Quoc-Phuong Huynh1,2, and Hai Truong2
1Institute for Application Oriented Knowledge Processing (FAW), Johannes
Kepler University Linz, Austria
2Computer
Science and Engineering, HCMC University of Technology, Vietnam
Abstract: Nowadays, biometric recognition has
been widely applied in various aspects of security applications because of its
safety and convenience. However, unlike passwords or tokens, biometric features
are naturally noisy and cannot be revoked once they are compromised. Overcoming
these two weaknesses is an essential and principal demand. With a hybrid
approach, we propose a scheme that combines the Artificial Neural Network (ANN)
and the Secure Sketch concept to generate strong keys from a biometric trait
while guaranteeing revocability, template protection and noisy tolerance
properties. The ANN with high noisy tolerance capacity enhances the recognition
by learning the distinct features of a person, assures the revocable and
non-invertible properties for the transformed template. The error correction
ability of a Secure Sketch concept’s construction significantly reduces the
false rejection rate for the enroller. To assess the scheme’s security, the average
remaining entropy is measured on the generated keys. Empirical experiments with
standard datasets demonstrate that our scheme is able to achieve a good
trade-off between the security and the recognition performance when being
applied with the face biometrics.
Keywords: Biometric cryptography, biometric
template protection, ANN, Secure Sketch, remaining entropy.
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