Adding localization information in a fingerprint binary feature vector representation

Abstract

At BTAS'10, a new framework to transform a fingerprint minutiae template into a binary feature vector of fixed length is described. A fingerprint is characterized by its similarity with a fixed number set of representative local minutiae vicinities. This approach by representative leads to a fixed length binary representation, and, as the approach is local, it enables to deal with local distortions that may occur between two acquisitions. We extend this construction to incorporate additional information in the binary vector, in particular on localization of the vicinities. We explore the use of position and orientation information. The performance improvement is promising for utilization into fast identification algorithms or into privacy protection algorithms.