In 1999 Juels and Wattenberg introduced the fuzzy commitment scheme. Fuzzy commitment is a particular realization of a
binary biometric secrecy system with a chosen secret key. Three cases of biometric sources are considered, i.e. memoryless
and totally-symmetric biometric sources, memoryless and
input-symmetric biometric sources, and memoryless biometric
sources. It is shown that fuzzy commitment is only optimal for memoryless totally-symmetric biometric sources and only
at the maximum secret-key rate. Moreover, it is demonstrated that for memoryless biometric sources, which are not inputsymmetric,
the fuzzy commitment scheme leaks information on both the secret key and the biometric data. Finally, a
number of coding techniques are investigated for the case of
totally-symmetric memoryless biometric data statistics.
One of the important stages of fingerprint recognition is the registration of the fingerprints with respect to the original template. This is not a straightforward task as fingerprint images may have been subject to rotations and translations. Popular
techniques for fingerprint registration use a reference point to achieve alignment. The drawback of existing methods of
core/reference point detection is their poor performance on rotated images. In this paper, we propose a new approach for
rotation invariant and reliable reference point detection applicable to fingerprints of different quality and types. Our approach
is based on the integration of a directional vector field (representing the doubled ridge orientations in fingerprints)
over a closed contour. We define the reference point as the point of the highest curvature. Areas of high curvature in the fingerprint are characterized by large differences in the orientations and correspond to high curvatures in the directional vector fields. Closed contour integrals of orientation vector field, defined as above, over a circle centered around the reference point corresponds to maximal closed curve integrals, and the values associated with such integrals are rotation invariant. Experimental results prove that with the proposed approach we can locate the reference point with high accuracy. Comparison with existing methods is provided.
Unique Biometric Identifiers offer a very convenient way for human identification and authentication. In contrast to passwords they have hence the advantage that they can not be forgotten or lost.
In order to set-up a biometric identification/authentication system, reference data have to be stored in a central database. As biometric identifiers are unique for a human being, the derived templates comprise unique, sensitive and therefore private information about
a person. This is why many people are reluctant to accept a system based on biometric identification. Consequently, the stored templates have to be handled with care and protected against misuse [1, 2, 3, 4, 5, 6]. It is clear that techniques from cryptography can be
used to achieve privacy. However, as biometric data are noisy, and cryptographic functions are by construction very sensitive to small changes in their input, and hence one can not apply those crypto techniques straightforwardly. In this paper we show the feasibility of the techniques developed in ,  by applying them to experimental biometric data. As biometric identifier we have choosen the shape of the inner ear-canal, which is obtained by measuring the headphone-to-ear-canal Transfer Functions (HpTFs) which are known to be person dependent .