Enhanced Locality-Sensitive Hashing for Fingerprint Forensics over Large Multi-sensor Databases

Abstract

Searching the identity of an unknown fingerprint over large databases is very challenging. Minutia Cylinder-Code (MCC) has been proved to be very effective in mapping a minutiae-based representation (positions and directions only) into a set of fixed-length transformation-invariant binary vectors. Based on MCC, a Locality-Sensitive Hashing (LSH) scheme has been designed to index fingerprint in large databases, which uses a numerical approximation for the similarity between MCC vectors. However, the LSH scheme is not robust enough when there is certain distortion between template and searched samples, such as fingerprints captured by multi-sensors. In this paper, we propose a finer hash bit selection method based on LSH. Besides, we take into consideration another feature - the single maximum collision for indexing and fuse the candidate lists produced by both indexing methods to produce the final candidate list. Experimentations carried out on our collected multi-sensor database (2D and 3D databases) show that the proposed indexing approach greatly improves the performance of fingerprint indexing. Extensive evaluation was also conducted on some public benchmark databases for fingerprint indexing, and the results demonstrated that the new approach outperforms existing ones in almost all the cases.