Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is 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. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points.

A "physical uncloneable function" (PUF) is a function that is realized by a physical system, such that the function is easy to evaluate but the physical system is hard to characterize. PUFs were introduced as a cost-effective way of generating secure keys for cryptographic purposes. A PUF is a physical system designed such that it interacts in a complicated way with stimuli (challenges) and leads to unique but unpredictable responses. A PUF is similar to a keyed hash function. The key is the physical system consisting of many "random" components.