Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A secure biometric authentication scheme based on robust hashing
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
Generating Cancelable Fingerprint Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetric hash functions for secure fingerprint biometric systems
Pattern Recognition Letters
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Parameterized geometric alignment for minutiae-based fingerprint template protection
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
A hybrid biometric cryptosystem for securing fingerprint minutiae templates
Pattern Recognition Letters
An alignment-free fingerprint cryptosystem based on fuzzy vault scheme
Journal of Network and Computer Applications
Cancelable fingerprint templates using minutiae-based bit-strings
Journal of Network and Computer Applications
Combination of Symmetric Hash Functions for Secure Fingerprint Matching
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A key binding system based on n-nearest minutiae structure of fingerprint
Pattern Recognition Letters
Pair-polar coordinate-based cancelable fingerprint templates
Pattern Recognition
Fingerprint-Based Fuzzy Vault: Implementation and Performance
IEEE Transactions on Information Forensics and Security
Alignment-Free Cancelable Fingerprint Templates Based on Local Minutiae Information
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Abstraction of a fingerprint in the form of a hash can be used for secure authentication. The main challenge is in finding the right choice of features which remain relatively invariant to distortions such as rotation, translation and minutiae insertions and deletions, while at the same time capturing the diversity across users. In this paper, an alignment-free novel fingerprint hashing algorithm is proposed which uses a graph comprising of the inter-minutia minimum distance vectors originating from the core point as a feature set called the minimum distance graph. Matching of hashes has been implemented using a corresponding search algorithm. Based on the experiments conducted on the FVC2002-DB1a and FVC2002-DB2a databases, we obtained an equal error rate of 2.27%. The computational cost associated with our fingerprint hash generation and matching processes is relatively low, despite its success in capturing the minutia positional variations across users.