A robust alignment-free fingerprint hashing algorithm based on minimum distance graphs

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.

► Authors propose a fingerprint hashing scheme based on Minimum Distance Graphs (MDGs).
► Extracted MDG features are invariant to rotation, translation and noise.
► Experiments conducted on FVC2002-DB1a and DB2a databases gave a minimum EER of 2.27%.
► Cancelable MDGs can be generated using a minutia perturbation model.
► MDG hash security is approx. 133 bits for FVC2002-DB1 and 162 bits for FVC2002-DB2.