Runtime Optimization of Identification Event in ECG Based Biometric Authentication

TL;DR

This paper presents a model that optimizes the runtime of ECG-based biometric authentication, achieving significant speed improvements while maintaining perfect accuracy, thus enhancing its practicality and competitiveness.

Contribution

The paper introduces a novel runtime optimization model for ECG biometric identification, significantly reducing processing time without sacrificing accuracy.

Findings

Achieved up to 79.26% reduction in identification time.

Maintained 100% accuracy in ECG biometric authentication.

Demonstrated the model's effectiveness in improving performance.

Abstract

Biometric Authentication has become a very popular method for different state-of-the-art security architectures. Albeit the ubiquitous acceptance and constant development of trivial biometric authentication methods such as fingerprint, palm-print, retinal scan etc., the possibility of producing a highly competitive performance from somewhat less-popular methods still remains. Electrocardiogram (ECG) based biometric authentication is such a method, which, despite its limited appearance in earlier research works, are currently being observed as equivalently high-performing as other trivial popular methods. In this paper, we have proposed a model to optimize the runtime of identification event in ECG based biometric authentication and we have achieved a maximum of 79.26% time reduction with 100% accuracy.