Multibiometric Cryptosystems Based on Feature-Level Fusion(2012)

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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.

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.

Click here to download Multibiometric Cryptosystems Based on Feature-Level Fusion(2012) source code