Runtime Optimization of Identification Event in ECG Based Biometric Authentication

Biometric Authentication has become a very popular method for different state-of-the-art security architectures. Albeit the ubiquitous acceptance and constant development in trivial biometric authentication methods such as fingerprint, palm-print, retinal scan etc., the possibility of producing highly competitive performance from somewhat less-popular methods still remains. Electrocardiogram (ECG) based multi-staged biometric authentication is such a method, which, despite its limited appearance in earlier research works, are currently being observed as equally high-performing as other trivial popular methods. The identification stage of this method suffers from requiring a high runtime, due to cross-matching of the new data with every single template data stored in the database, especially when dealing with huge amount of data. To solve this unaddressed problem, in this paper, we have proposed a K-means clustering based novel method where all the template data are clustered based on similarity, and only the most similar data cluster is searched instead of whole dataset during the identification event. Using our method we have achieved a maximum of 79.26% time reduction with 100% accuracy.