Fingermark Quality Assessment: An Open-Source Toolbox

Abstract

Fingermark quality assessment is an important step in a forensic fingerprint identification process. Often done in the scope of criminal investigation, it is performed by trained fingerprint examiners whose quality assessment can be rather subjective. The goal of this work is to develop an automated fingermark quality assessment tool, which would assist the fingermark examiners in their work. In this paper, we present a fast, open-source, and well documented fingermark quality assessment toolbox, which contains more than 20 algorithms for feature extraction, segmentation, and enhancement of fingermark images. We demonstrate the utility of the toolbox by assembling a feature vector and training various baseline machine learning models, capable of predicting the quality of fingermark images with high accuracy.