Multibiometric
Cryptosystems Based on Feature-Level Fusion
ABSTRACT:
Multi-biometric systems
are being increasingly deployed 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 uni-biometric
systems. However, multi-biometric 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 include: 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 security and matching performance compared to their uni-biometric
counterparts.
ARCHITECTURE:
EXISTING SYSTEM:
(i) Non-invertibility—given a secure template, it must be
computationally difficult to find a biometric feature set that will match with
the given template, and
(ii) Revocability— given two secure templates generated from the same
biometric data, it must be computationally hard to identify that they are
derived from the same data or obtain the original biometric data.
DISADVANTAGES OF EXISTING SYSTEM:
While multi-biometric systems have
improved the accuracy and reliability of biometric systems, sufficient
attention has not been paid to security of multi-biometric templates. Though a biometric
system can be compromised in a number of ways, leakage of biometric template
information to unauthorized individuals constitutes a serious security and
privacy threat due to the following two reasons:
1) Intrusion attack: If an
attacker can hack into a biometric database, he can easily obtain the stored
biometric information of a user. This information can be used to gain unauthorized
access to the system by either reverse engineering the template to create a
physical spoof or replaying the stolen template.
2) Function creep: An adversary
can exploit the biometric template information for unintended purposes (e.g.,
covertly track a user across different applications by cross-matching the
templates from the associated databases) leading to violation of user privacy. Security
of multi-biometric templates is especially crucial as they contain information
regarding multiple traits of the same user.
PROPOSED SYSTEM:
We propose a feature-level fusion
framework to simultaneously secure multiple templates of a user using biometric
cryptosystems. To demonstrate the viability of this framework, we propose
simple algorithms for the following three tasks:
1) Converting different biometric
representations into a common representation space using various embedding algorithms:
(a) binary strings to point-sets, (b) point-sets to binary strings, and (c)
fixed-length real-valued vectors to binary strings.
2) Fusing different features into a
single multibiometric template that can be secured using an appropriate biometric
cryptosystem such as fuzzy vault and fuzzy commitment; efficient decoding
strategies for these biometric cryptosystems are also proposed.
3) Incorporating a minimum matching
constraint for each trait, in order to counter the possibility of an attacker gaining
illegitimate access to the secure system by simply guessing/knowing only a
subset of the biometric traits
ADVANTAGES OF PROPOSED SYSTEM:
ü Compared
to uni-biometric systems that rely on a single biometric trait, multi-biometric
systems can provide higher recognition accuracy and larger population coverage.
ü Consequently,
multi-biometric systems are being widely adopted in many large-scale
identification systems.
MODULES:
Fingerprint feature
Module
IRIS feature Module
Feature-Level Fusion
Module
Secure data forwarding
Module
Performance Evaluation
Module
MODULES
DESCRIPTION:
Fingerprint
feature Module
In this module, Fingerprint
minutiae are extracted obtain the binary string representation from the
minutiae set. First the user has to upload and select the fingerprint images
from the sample database. Then the Finger print feature are loaded into the
system. Then this module, extracts the fingerprint features.
IRIS
feature Module
In this module, the
binary Iris Code features are extracted. The user has to upload and select the
IRIS images from the sample database. Then the IRIS feature are loaded into the
system. In order to reduce the dimensionality of the iris code and remove the
redundancy present in the code, LDA is applied to the IRIS code features. Then
the binary IRIS code features are extracted.
Feature-Level
Fusion Module
We propose a feature-level fusion
framework to simultaneously secure multiple templates of a user using biometric
cryptosystems. To demonstrate the viability of this framework, we propose
simple algorithms for the following three tasks:
1) Converting different biometric
representations into a common representation space using various embedding algorithms:
(a) binary strings to point-sets, (b) point-sets to binary strings, and (c)
fixed-length real-valued vectors to binary strings.
2) Fusing different features into a
single multi-biometric template that can be secured using an appropriate biometric
cryptosystem such as fuzzy vault and fuzzy commitment; efficient decoding
strategies for these biometric cryptosystems are also proposed.
3) Incorporating a minimum matching
constraint for each trait, in order to counter the possibility of an attacker gaining
illegitimate access to the secure system by simply guessing/knowing only a
subset of the biometric traits.
Secure
data forwarding Module
In this module the data
is forwarded to the Server securely. The data from the client module is
sent/forwarded to the server module. Where, the user has to give the IP address
of the server to send the data from client to server. After providing the IP
address, the module starts working and the data is sent to the server securely.
In the server side, the data reaches and then multi-biometric images are
reconstructed there again.
Performance
Evaluation Module
We evaluate the trade-off between
recognition accuracy and security of the proposed multibiometric cryptosystems
using
To validate the constrained
multibiometric cryptosystem, we implemented a system consisting of iris and
fingerprint modalities, where minimum matching constraints are imposed for the
fingerprint modality. We further assume that the adversary has knowledge about
the iris biometric, i.e., he has access to some iris image of the enrolled
user. In this experiment, a multibiometric fuzzy commitment is implemented and
a secondary representation of fingerprints is obtained using minutiae aggregates.
Minutiae are employed as the primary fingerprint representation, and
hence a fuzzy vault is used in the second stage. The degree of polynomial for
the fuzzy vault is selected such that the sum of security in bits and GAR in
percentage of the resulting system is maximized. Using this constrained multi-biometric
cryptosystem, it is possible to achieve a security of 35 bits even if the iris
features of a genuine user are known to the adversary. However, the GAR for
this scenario is only 15% compared to a GAR of 70%, when no constraints were imposed
on the fingerprint modality.
HARDWARE
REQUIREMENTS
•
SYSTEM : Pentium IV 2.4 GHz
•
HARD
DISK : 40 GB
•
FLOPPY
DRIVE : 1.44 MB
•
MONITOR : 15 VGA colour
•
MOUSE : Logitech.
•
RAM : 256 MB
•
KEYBOARD :
110 keys enhanced.
SOFTWARE
REQUIREMENTS
•
Operating system :- Windows XP
Professional
•
Front End :- Microsoft Visual Studio .Net 2008
•
Coding Language : - C# .NET.
•
Database :-
SQL Server 2005
REFERENCE:
Abhishek Nagar, Student Member, IEEE, Karthik Nandakumar, Member, IEEE, and AnilK. Jain, Fellow, IEEE, “Multibiometric
Cryptosystems Based on Feature-Level Fusion”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 7, NO. 1,
FEBRUARY 2012.
