Abstract
In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.
Highlights
At present, we are experiencing emerging digitization in most aspects of our lives.Day-to-day, use online applications and services such as mobile banking, social networking, online stock exchange, and trading or email services, with little apprehension about storing personal confidential information on our devices or client servers
To achieve a higher acceleration of the training process, which is usually a bottleneck while operating deep networks with many layers, our proposed models are developed in the Tensorflow deep learning library, which can be executed on a graphics processing unit (GPU)
Before we investigated the performance of our proposed method, we studied it in comparison to conventional recurrent neural network (RNN)-based methods, namely, traditional RNN, RNN with long short-term memory (LSTM) gates, and RNN with GRU gates over four datasets with QRS complex
Summary
Day-to-day, use online applications and services such as mobile banking, social networking, online stock exchange, and trading or email services, with little apprehension about storing personal confidential information on our devices or client servers. We are faced with new attacks and exploits, as well as unauthorized access to sensitive information and devices by malicious viruses or hosts. It is incredible that large populations of users still rely on numerous types or particular sets of passwords, which have been used for authorized access since the earliest era of computing. There has been a shift in attention towards biometric security systems. These security applications facilitate the identification of an individual using their distinct biological characteristics instead of a set of numerical or alphabetical passwords. The most widespread techniques use fingerprint, iris, and facial recognition approaches, and are normally found in smart devices [1]
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