Adaptive fingerprint enhancement and identificationusing linear parametric models

摘要

Historically, due to its uniqueness and immutability, fingerprints have been used as evidence in criminal cases and insecurity identification as well as authorization verification applications. In this research, adaptive linear DWTmodels are developed to describe the fingerprint features (DWT coefficients) to be identified. The proposed modelcan be used to enhance the fingerprint characteristics identified from fingerprint images to improve recognition. Thisadaptive model identification technique is then applied to degraded or incomplete fingerprint images to demonstratethe efficacy of the technique under non-ideal conditions. The performance of the method is then compared topreviously published research by the authors on identification of degraded fingerprints using PCA-and ICA-basedfeatures.