An IoT Real-Time Biometric Authentication System Based on ECG Fiducial Extracted Features Using Discrete Cosine Transform

TL;DR

This paper proposes a real-time IoT biometric authentication system using ECG signals and Discrete Cosine Transform for feature extraction, achieving high accuracy and fast processing suitable for practical applications.

Contribution

It introduces a novel real-time ECG-based authentication system utilizing DCT features, emphasizing its suitability for IoT environments with rapid processing.

Findings

Achieved 97.78% authentication accuracy

Processing time of approximately 1.21 seconds

Effective for real-time IoT applications

Abstract

The conventional authentication technologies, like RFID tags and authentication cards/badges, suffer from different weaknesses, therefore a prompt replacement to use biometric method of authentication should be applied instead. Biometrics, such as fingerprints, voices, and ECG signals, are unique human characters that can be used for authentication processing. In this work, we present an IoT real-time authentication system based on using extracted ECG features to identify the unknown persons. The Discrete Cosine Transform (DCT) is used as an ECG feature extraction, where it has better characteristics for real-time system implementations. There are a substantial number of researches with a high accuracy of authentication, but most of them ignore the real-time capability of authenticating individuals. With the accuracy rate of 97.78% at around 1.21 seconds of processing time, the proposed system is more suitable for use in many applications that require fast and reliable authentication processing demands.