Spectral minutiae: A fixed-length representation of a minutiae set
Minutiae, which are the endpoints and bifurcations of fingerprint ridges, allow a very discriminative classification of fingerprints. However, a minutiae set is an unordered set and the minutiae locations suffer from various deformations such as translation, rotation and scaling. In this paper, we introduce a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. By applying the spectral minutiae representation, we can combine the fingerprint recognition system with a template protection scheme, which requires a fixed-length feature vector. This paper also presents two spectral minutiae matching algorithms and shows experimental results.