Fingerprint Enhancement using Iterative Contextual Filtering for Fingerprint Matching

Abstract

Fingerprint matching depends on the quality of the fingerprint images. When fingerprint image quality is low, it can degrade the performance of fingerprint matching significantly. Fingerprint images are often contaminated by noise. Therefore, image quality is crucial for fingerprint matching. In this paper, an image enhancement algorithm in which contextual filtering is applied iteratively to a fingerprint image has been proposed. The main idea of the algorithm is to iterate the output of the Gabor filter to get better enhancement and matching performance. The result of the algorithm has five filtered images due to five times iteration. It showed that the proposed method is significantly better based on Equal Error Rate (EER) compared to the Gabor filter and the modified Gabor filter. The proposed method surpassed the Gabor filter by 3.08 % and the modified Gabor filter by 2.95 %.