Filter Design and Performance Evaluation for Fingerprint Image Segmentation

TL;DR

This paper introduces a new fingerprint segmentation method called FDB, creates a large benchmark dataset, and demonstrates its superior performance over existing algorithms, enhancing fingerprint recognition accuracy.

Contribution

The paper presents a novel FDB segmentation technique, provides a comprehensive benchmark dataset, and systematically compares performance with existing methods, showing clear improvements.

Findings

FDB outperforms four widely used segmentation algorithms

Benchmark dataset of 10,560 images established

Favorable results in accuracy and robustness

Abstract

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.