Runtime Optimization of Identification Event in ECG Based Biometric Authentication
This work addresses runtime efficiency for ECG biometric authentication systems, which is an incremental improvement over existing methods.
The paper tackled the problem of slow runtime in ECG-based biometric authentication by proposing an optimization model, achieving a 79.26% time reduction while maintaining 100% accuracy.
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.