A Review Study of Heartbeat Biometric Authentication

Abstract

Today, life engages technology in multiple ways, thus authentication in human technologies is very important. Secure and reliable authentication is in high demand. However, traditional methods for authentication such as passwords and tokens are now outdated because it is possible to steal, lose and share such authentication methods. Current research shows that one of the best methods for authenticating human beings is biometrics. In this paper, the heartbeat biometric, also called Electrocardiographic (ECG), is proposed. The heartbeat biometric is chosen because unique human ECGs cannot be falsely created or replicated. While other biometric methods, such as face recognition, can be compromised by user photographs, or fingerprints, which can be compromised by use of fake fingers, the ECG signal is based on the individualized mechanical movements of each human heart, which features contain unique physiological information. The purpose of this paper is, then, to review various relevant, recent works that study the heartbeat biometric to find the best biometrics features, given the extractions and classification algorithms for the heartbeat biometric signal. This paper concludes that the morphological (P wave) feature is recommended as the most important feature and the Neural Network (NN) classifier is the most reliable classification with the highest performance accuracy for heartbeat biometric. Therefore, to achieve highest accuracy and result for authenticating through heartbeat biometric, it is recommended to consider the mentioned feature extractions and classification.