Hybrid Fingerprint Orientation Map (HFOM) with Data Security

A repo. containing code for research work

Hybrid Fingerprint Orientation Map (HFOM) with Data Security

fingerprint.jpg

Introduction

Due to the property of uniqueness, biometric information can identify any individual. This specialty of fingerprint biometric information has frequently been utilized in various fields over the past several years. This portal provides a broad-level overview of how we leverage the qualities of fingerprints to propose a better data security infrastructure.

It is the documentation for the project entitled “Asymmetric Encryption Techniques for Data Embedding and Authentication in Fingerprints Using Eigen Space-Based Modelling”, by Dr. Sinnu S. Thomas and Ravi Prakash. The abstract is provided in the following section of this page.

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’ labeled 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. We make the code publicly available for further research.”

Footnote:

* The complete source code is available at the GitHub portal of this website, i.e. https://github.com/ravi-prakash1907/HFOM-with-Data-Security. However, currently it is accessible to a limited audience only. In order to get access to the source code explicitly, feel free to contact the authors via e-mail. Please refer to the authors’ profile (in the introduction) for the contact details.