A Security-Enhanced Alignment-Free Fuzzy Vault-Based Fingerprint Cryptosystem Using Pair-Polar Minutiae Structures

ABSTRACT:

Alignment-free fingerprint cryptosystems perform matching using relative information between minutiae, e.g., local minutiae structures, is promising, because it can avoid the recognition errors and information leakage caused by template alignment/registration. However, as most local minutiae structures only contain relative information of a few minutiae in a local region, they are less discriminative than the global minutiae pattern. Besides, the similarity measures for trivially/coarsely quantized features in the existing work cannot provide a robust way to deal with nonlinear distortions, a common form of intra-class variation. As a result, the recognition accuracy of current alignment-free fingerprint cryptosystems is unsatisfying. In this paper, we propose an alignment-free fuzzy vault-based finger-print cryptosystem using highly discriminative pair-polar (P-P) minutiae structures. The fine quantization used in our system can largely retain information about a fingerprint template and enables the direct use of a traditional, well-established minutiae matcher. In terms of template/key protection, the proposed system fuses cancelable biometrics and bio cryptography. Transforming the P-P minutiae structures before encoding destroys the correlations between them, and can provide privacy-enhancing features, such as revocability and protection against cross-matching by setting distinct transformation seeds for different applications. The comparison with other minutiae-based fingerprint cryptosystems shows that the proposed system performs favorably on selected publicly available databases and has strong security.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• In existing system original minutiae features are coarsely quantized and then matching is performed via some sort of similarity measure.

DISADVANTAGES OF EXISTING SYSTEM:

• The main problem with this technique is that coarse quantization will lead to discrimination/information loss of the original features because short binary strings are insufficient to represent features from a large domain.

• Also, traditional and well-established similarity measures specially designed to deal with fingerprint intra-class variations cannot be applied directly to coarsely-quantized features.

PROPOSED SYSTEM:

We propose a security-enhanced alignment-free fuzzy vault-based fingerprint cryptosystem using pair-polar (P-P) minutiae structures. The main contributions are as follows.

• Compared with other local minutiae structures that contain only relative information of a few minutiae in a local region, as the P-P minutiae structure used in the proposed system describes the relationships between a reference minutia and all the others in a fingerprint within its polar coordinate space, it is more discriminative (experimentally proven).

• A well-established minutiae matcher in global minutiae matching algorithms is seamlessly transformed into a transformation-invariant feature-applicable version and information about the original features is largely retained using a fine quantization, which only removes the decimal parts of the features.

• Unlike many fuzzy vault constructions that choose chaff points separated by a minimum distance d from any genuine point and previously added chaff point, where d is the distance inside which a query feature and vault point are considered matched during verification, the proposed vault selects both genuine and chaff features greater than 2d away from each other. As this design removes the probability that a query feature matches multiple points in the vault, decoding time is signifycantly reduced.

• For security enhancement, each P-P minutiae structure is transformed before being encoded into the fuzzy vault. The transformation functions take a pre-set seed and an invariant value extracted from each reference minutia as parameters. This approach fully combines the advantages of both cancelable biometrics and biocryptography. Firstly, transforming the P-P minutiae structures before encoding destroys the correlations between them and can provide privacy-enhancing features, such as revocability and protection against cross-matching attacks, by setting distinct seeds for different applications. Secondly, the transformed genuine features are blurred by a greater number of chaff points, which further increases the difficulty of deriving the original features from the transformed ones.

ADVANTAGES OF PROPOSED SYSTEM:

• Improve the recognition accuracy.

• Our system having strong security.

MODULES:

1. Encoding Stage
2. Decoding Stage

MODULE DESCRIPTION:

Fuzzy vault based fingerprint cryptosystem.

Description of Fuzzy Vault:

• Alice places a secret K in a vault and locks it with unordered set A.

• Bob uses an unordered set B to unlock the vault and access K.

• Successful if B and A overlap substantially

1. Encoding Stage:

• In this stage, secret data is encoded by pair-polar template finger minutiae structure points.

• Given template finger is used for encode of secret data. We first extract template minutiae set (minutiae points) from template finger.

• From this set, we choose only 30 well-separated genuine minutiae, i.e., the minimum distance between each is greater than a predefined threshold (d). These 30 genuine minutiae are choosed in pair polar structure of template. We use ROI for construct pair polar structure. After extract the suppressed minutiae points A (30 minutiae points), randomly generate the minutiae points called as chaff points (CM) for increase the security of fuzzy vault.

In our work, final key binding created by two levels.

• In first level, given secret data is divided into n segments (ex.124 is original data, segmented data is 1 2 4. And also secret data length is maximum five digits). Generate polynomial for given secret data. After that polynomial projection is computed from secret data and suppressed minutiae points (A, P (A) is an original minutiae point’s fuzzy vault). Then add chaff points to evaluated polynomial projection for create final fuzzy vault (A, P (A) U (CM)).

• In second level, Shamir secret sharing is applied our secret data. In this segmented secret data is used for generate Shamir Pieces one by one. Finally generate our final key binding by combining first level of final fuzzy vault and second level of Shamir pieces.

2. Decoding Stage:

• In this stage, encoded secret data is extracted by pair-polar query finger minutiae structure points.

• Same reverse process is applied for decoding. Given query finger is used for decode of secret data. We first extract query minutiae set (minutiae points) from query finger. From this set, we choose only 30 well-separated genuine minutiae, i.e., the minimum distance between each is greater than a predefined threshold (d). These 30 genuine minutiae are choosed in pair polar structure of query. We use ROI for construct pair polar structure. After extract the suppressed minutiae points B (30 minutiae points), randomly generate the minutiae points called as chaff points (CM).

• Then Shamir reconstruction is applied to Shamir pieces. Shamir pieces are getting by final key binding. In Shamir reconstruction, Lagrange interpolation is used for extract the segmented data. After extract the segmented data, generate polynomial for extracted segmented data and also generate polynomial projection from extracted segmented data and suppressed minutiae points (B, P (B)).

• Then find difference or matching points between template final fuzzy vault and query final fuzzy vault. If overlapping of template final fuzzy vault and query final fuzzy vault, secret data was extracted and also user is a correct (same person). If not overlapping, secret data was not extracted and also user is not a correct.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System          : Pentium IV 2.4 GHz.

• Hard Disk     : 40 GB.

• Floppy Drive : 1.44 Mb.

• Monitor          : 15 VGA Colour.

• Mouse            : Logitech.

• Ram                : 512 Mb.

SOFTWARE REQUIREMENTS:

• Operating system : Windows XP/7.

• Coding Language : MATLAB

• Tool                       : MATLAB R2013A

REFERENCE:

Cai Li, Member, IEEE, and Jiankun Hu, “A Security-Enhanced Alignment-Free Fuzzy Vault-Based Fingerprint Cryptosystem Using Pair-Polar Minutiae Structures”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 3, MARCH 2016.