eCite Digital Repository

ECG-based biometric human identification based on backpropagation neural network


Lynn, HM and Yeom, S and Kim, P, ECG-based biometric human identification based on backpropagation neural network, Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems, 9-12 October 2018, Honolulu, HI, USA, pp. 6-10. ISBN 978-1-4503-5885-9 (2018) [Refereed Conference Paper]

Copyright Statement

Copyright 2018 Association for Computing Machinery

DOI: doi:10.1145/3264746.3264760


Biometric human identifications are expansively reshaping security applications in the emerging sophisticated era of smart devices. To inflate the level of security and privacy demands, human physiological signal based human identification and authentication systems are getting tremendous attention. This study focuses on producing feasible amount of segmented signals from a source signal for training dataset, and integrating 2-layer framework backpropagation neural network to handle the great amount of classes for identification without hesitation. The results suggest that the proposed method surpasses the recent technique with the similar architecture, and possesses more advantages in terms of computational complexity and high performance compared with the previously reported study.

Item Details

Item Type:Refereed Conference Paper
Keywords:ECG, backpropagation neural network, biometrics human identification, machine learning, deep learning, supervised learning, classification
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the environmental sciences
UTAS Author:Yeom, S (Dr Soonja Yeom)
ID Code:129147
Year Published:2018
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2018-11-12
Last Modified:2019-03-25

Repository Staff Only: item control page