Unbalanced Fingerprint Classification for Hybrid Fingerprint Orientation Maps

TL;DR

This paper presents a new multi-layered fuzzy logic classifier for fingerprint classification, addressing class imbalance with an eigenvector-based sampling method, and introduces a hybrid fingerprint orientation map for biometric data protection.

Contribution

It introduces a novel fuzzy logic classifier and an adaptive eigenvector-based sampling algorithm for improved fingerprint classification and generates a hybrid fingerprint orientation map for biometric security.

Findings

Improved classification accuracy over neural-network methods

Effective handling of class imbalance with eigenvector sampling

HFOM as a virtual proxy enhances biometric data protection

Abstract

This paper introduces a novel fingerprint classification technique based on a multi-layered fuzzy logic classifier. We target the cause of missed detection by identifying the fingerprints at an early stage among dry, standard, and wet. Scanned images are classified based on clarity correlated with the proposed feature points. We also propose a novel adaptive algorithm based on eigenvector space for generating new samples to overcome the multiclass imbalance. Proposed methods improve the performance of ensemble learners. It was also found that the new approach performs better than the neural-network based classification methods. Early-stage improvements give a suitable dataset for fingerprint detection models. Leveraging the novel classifier, the best set of `standard' labelled fingerprints is used to generate a unique hybrid fingerprint orientation map (HFOM). We introduce a novel min-rotate max-flow optimization method inspired by the min-cut max-flow algorithm. The unique properties of HFOM generation introduce a new use case for biometric data protection by using HFOM as a virtual proxy of fingerprints.