A Comprehensive Method for Fingerprint Classification Based on Gabor Filters and Machine Learning

Abstract

The fingerprint is a valuable tool for both forensic analysis and community security. Stateof-the-art fingerprint classification methods tend to ignore image quality enhancement as well as use high-dimension feature sets resulting in unnecessary computational complexities. To address these issues, this study proposes an efficient fingerprint classification method that combines Histogram of Oriented Gradient (HOG) and Gabor Filter features with Random Forest (RF) and Naïve Bayes (NAÏVE) classifiers. It sequentially preprocesses the input with a series of receiving functions that enhance the image, such as grayscale, morphological, and binary. The method’s performance was evaluated on the SOCOFing dataset, and 99% classification accuracy was demonstrated using the Gabor-Naïve approach, surpassing some sophisticated techniques in terms of accuracy and computational efficiency. This work contributes to the field by addressing gaps in image enhancement and feature dimensionality, offering a robust solution for authenticating and distinguishing altered fingerprints. Future research could build on this by examining different classifiers for additional optimization and testing the methodology on a variety of datasets.