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ECG-based biometric human identification based on backpropagation neural network

conference contribution
posted on 2023-05-23, 13:46 authored by Lynn, HM, Soonja YeomSoonja Yeom, Kim, P
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.

History

Publication title

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems

Pagination

6-10

ISBN

978-1-4503-5885-9

Department/School

School of Information and Communication Technology

Publisher

ACM

Place of publication

USA

Event title

2018 Conference on Research in Adaptive and Convergent Systems (RACS '18)

Event Venue

Honolulu, HI, USA

Date of Event (Start Date)

2018-10-09

Date of Event (End Date)

2018-10-12

Rights statement

Copyright 2018 Association for Computing Machinery

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the environmental sciences

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    University Of Tasmania

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