Attack Resistant Fingerprint Recognition System Using Biocryptography

Abstract

Biometric is one of the unique features are used for the person identification process. The biometric features like fingerprints, palmprints, eye retina and face recognition are used for the authentication process. Maintenance and verification of biometric features is the complex task for the person identification applications. Alignment-free fingerprint cryptosystems perform matching using relative information between minutiae features. Minutiae structure can avoid the recognition errors and information leakage caused by template alignment/registration. 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.
The similarity measures for trivially/coarsely quantized features cannot provide a robust way to deal with nonlinear distortions, a common form of intraclass variation. The recognition accuracy of current alignment-free fingerprint cryptosystems is unsatisfying. The system uses an alignment-free fuzzy vault-based fingerprint cryptosystem using highly discriminative pair-polar (P-P) minutiae structures. The adequate quantization used in the system can largely maintain information about a fingerprint template and enables the direct use of a traditional, well-established minutiae matcher. In terms of template/key protection, the system fuses cancelable biometrics and biocryptography.
Transforming the P-P minutiae structures before encoding abolishes 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 fingerprint recognition system is enhanced to handle the brute force attacks. The biocryptography mechanism can be integrated with other data security operations. The system is tuned to perform the biometric based security operations under distributed environment.