In this work we present a multibiometric face recognition framework based on combining information from 2D with 3D
facial features. The 3D biometrics channel is protected by a privacy enhancing technology, which uses error correcting
codes and cryptographic primitives to safeguard the privacy of the users of the biometric system at the same time
enabling accurate matching through fusion with 2D. Experiments are conducted to compare the matching performance of
such multibiometric systems with the individual biometric channels working alone and with unprotected multibiometric
systems. The results show that the proposed hybrid system incorporating template protection, match and in some cases
exceed the performance of corresponding unprotected equivalents, in addition to offering the additional privacy
protection.
Biohashing algorithms map biometric features randomly onto binary strings with user-specific tokenized random
numbers. In order to protect biometric data, these binary strings, the Biohashes, are not allowed to reveal much
information about the original biometric features. In the paper we analyse two Biohashing algorithms using scalar
randomization and random projection respectively. With scalar randomization, multiple bits can be extracted
from a single element in a feature vector. The average information rate of Biohashes is about 0.72. However,
Biohashes expose the statistic information about biometric feature, which can be used to estimate the original
feature. Using random projection method, a feature vector in n dimensional space can be converted into binary
strings with length of m (m ≤ n). Any feature vector can be converted into 2m different Biohashes. The random
projection can roughly preserve Hamming distance between Biohashes. Moreover, the direction information
about the original vector can be retrieved with Biohashes and the corresponding random vectors used in the
projection. Although Biohashing can efficiently randomize biometric features, combining more Biohashes of the
same user can leak essential information about the original feature.
As biometric recognition systems are widely applied in various application areas, security and privacy risks have
recently attracted the attention of the biometric community. Template protection techniques prevent stored
reference data from revealing private biometric information and enhance the security of biometrics systems
against attacks such as identity theft and cross matching. This paper concentrates on a template protection
algorithm that merges methods from cryptography, error correction coding and biometrics. The key component
of the algorithm is to convert biometric templates into binary vectors. It is shown that the binary vectors should
be robust, uniformly distributed, statistically independent and collision-free so that authentication performance
can be optimized and information leakage can be avoided. Depending on statistical character of the biometric
template, different approaches for transforming biometric templates into compact binary vectors are presented.
The proposed methods are integrated into a 3D face recognition system and tested on the 3D facial images of
the FRGC database. It is shown that the resulting binary vectors provide an authentication performance that
is similar to the original 3D face templates. A high security level is achieved with reasonable false acceptance
and false rejection rates of the system, based on an efficient statistical analysis. The algorithm estimates the
statistical character of biometric templates from a number of biometric samples in the enrollment database.
For the FRGC 3D face database, the small distinction of robustness and discriminative power between the
classification results under the assumption of uniquely distributed templates and the ones under the assumption
of Gaussian distributed templates is shown in our tests.
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