Unbalanced Fingerprint Classification for Hybrid Fingerprint Orientation Maps

TL;DR

A novel fingerprint classification technique based on a multi-layered fuzzy logic classifier that performs better than the neural-network based classification methods and introduces a novel min-rotate max-flow optimization method inspired by the min-cut max-flow algorithm.

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.

Figures (18)

• Figure 1: Proposed Approach.
• Figure 2: Dual-phase Dual-layered Architecture of Fuzzy Classifier.
• Figure 3: Creating HFOM from Pool of Classified Fingerprints.
• Figure 4: Block $\beta_{kl}$ as a set of overlapping sub-blocks $q_{\eta}$.
• Figure 5: Three possible orientation fields in $q_{\eta}$ having origin at $r'$.