31-03-2014, 03:26 PM
Multibiometric Cryptosystems Based on Feature-Level Fusion
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Abstract
Multibiometric systems are being increasingly de-
ployed in many large-scale biometric applications (e.g., FBI-IAFIS,
UIDAI system in India) because they have several advantages such
as lower error rates and larger population coverage compared to
unibiometric systems. However, multibiometric systems require
storage of multiple biometric templates (e.g., fingerprint, iris, and
face) for each user, which results in increased risk to user privacy
and system security. One method to protect individual templates
is to store only the secure sketch generated from the corresponding
template using a biometric cryptosystem. This requires storage
of multiple sketches. In this paper, we propose a feature-level
fusion framework to simultaneously protect multiple templates
of a user as a single secure sketch. Our main contributions in-
clude: 1) practical implementation of the proposed feature-level
fusion framework using two well-known biometric cryptosystems,
namely, fuzzy vault and fuzzy commitment, and 2) detailed analysis
of the trade-off between matching accuracy and security in the
proposed multibiometric cryptosystems based on two different
databases (one real and one virtual multimodal database), each
containing the three most popular biometric modalities, namely,
fingerprint, iris, and face. Experimental results show that both
the multibiometric cryptosystems proposed here have higher se-
curity and matching performance compared to their unibiometric
counterparts.
INTRODUCTION
MULTIBIOMETRIC systems accumulate evidence from
more than one biometric trait (e.g., face, fingerprint, and
iris) in order to recognize a person [1]. Compared to unibio-
metric systems that rely on a single biometric trait, multibio-
metric systems can provide higher recognition accuracy and
larger population coverage. Consequently, multibiometric sys-
tems are being widely adopted in many large-scale identification
systems, including the FBI’s IAFIS, the Department of Home-
land Security’s US-VISIT, and the Government of India’s UID.
A number of software and hardware multibiometric products
have also been introduced by biometric vendors [2], [3].
Evaluation of Fuzzy Commitment and Fuzzy Vault Schemes
The effectiveness of a biometric cryptosystem depends on the
matching performance and the template security. Matching per-
formance of a biometric system is usually quantified by the false
accept rate (FAR) and the genuine accept rate (GAR). In bio-
metric cryptosystems, matching is typically carried out using a
polynomial-time error correction decoding algorithm (compu-
tational complexity of the decoder is bounded by a polynomial
expression in the length of the codeword). Therefore, GAR (re-
spectively, FAR) can be defined as the proportion of genuine (re-
spectively, impostor) attempts that lead to successful decoding
in polynomial time.
CONCLUSION
We have proposed a feature-level fusion framework for the
design of multibiometric cryptosystems that simultaneously
protects the multiple templates of a user using a single secure
sketch. The feasibility of such a framework has been demon-
strated using both fuzzy vault and fuzzy commitment, which
are two of the most well-known biometric cryptosystems. We
have also proposed different embedding algorithms for trans-
forming biometric representations, efficient decoding strategies
for fuzzy vault and fuzzy commitment, and a mechanism to
impose constraints such as minimum matching requirement
for specific modalities in a multibiometric cryptosystem. A
realistic security analysis of the multibiometric cryptosys-
tems has also been conducted. Experiments on two different
multibiometric databases containing fingerprint, face, and iris
modalities demonstrate that it is indeed possible to improve
both the matching performance and template security using the
multibiometric cryptosystems.