Random local region descriptor (RLRD): A new method for fixed-length feature representation of fingerprint image and its application to template protection

Abstract

Minutia based features are the most widely used features in fingerprint recognition. However, the minutiae based fingerprint matching algorithms have some drawbacks that limit their applications in template protection. Because the minutia sets are unordered, it is difficult to determine the correspondence between two minutia sets and cannot be used in some known template protection schemes directly (e.g., fuzzy commitment, wrap around). In this paper, we propose a new fixed-length feature representation: random local region descriptor (RLRD) feature. The RLRD feature is extracted by randomly and uniformly selecting a set of points, where the order of points is determined by a random seed. For each point, a real fixed-length feature vector is extracted based on Tico’s sampling structure. The real RLRD feature vector can be further transformed into a bit vector for secure sketches working in the Hamming space. The experimental results on FVC2002 DB1 and DB2 show the advantages of the RLRD feature over some other fixed-length fingerprint feature vectors in terms of equal error rate (EER), genuine accept rate (GAR) and false accept rate (FAR).