Scale parameter-estimating method for adaptive fingerprint pore extraction model

Abstract

Sweat pores and other level 3 features have been proven to provide more discriminatory information about fingerprint characteristics, which is useful for personal identification especially in law enforcement applications. With the advent of high resolution (≥1000 ppi) fingerprint scanning equipment, sweat pores are attracting increasing attention in automatic fingerprint identification system (AFIS), where the extraction of pores is a critical step. This paper presents a scale parameter-estimating method in filtering-based pore extraction procedure. Pores are manually extracted from a 1000 ppi grey-level fingerprint image. The size and orientation of each detected pore are extracted together with local ridge width and orientation. The quantitative relation between the pore parameters (size and orientation) and local image parameters (ridge width and orientation) is statistically obtained. The pores are extracted by filtering fingerprint image with the new pore model, whose parameters are determined by local image parameters and the statistically established relation. Experiments conducted on high resolution fingerprints indicate that the new pore model gives good performance in pore extraction.