| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 832.2 KB | Adobe PDF |
Advisor(s)
Abstract(s)
Electrocardiogram (ECG) biometrics are a relatively recent trend in biometric recognition, with at least 13 years of development in peer-reviewed literature. Most of the proposed biometric techniques perform classifi-cation on features extracted from either heartbeats or from ECG based transformed signals. The best representation is yet to be decided. This paper studies an alternative representation, a dissimilarity space, based on the pairwise dissimilarity between templates and subjects' signals. Additionally, this representation can make use of ECG signals sourced from multiple leads. Configurations of three leads will be tested and contrasted with single-lead experiments. Using the same k-NN classifier the results proved superior to those obtained through a similar algorithm which does not employ a dissimilarity representation. The best Authentication EER went as low as 1:53% for a database employing 503 subjects. However, the employment of extra leads did not prove itself advantageous.
Description
Keywords
Authentication Biometrics Dissimilarity representation Dissimilarity space ECG Feature space Heartbeat Identification Nearest neighbor Segmentation
Citation
FERREIRA, Rui; [et al] - ECG biometrics using a dissimilarity space representation. BIOSIGNALS 2015, 8th International Conference on Bio-Inspired Systems and Signal Processing and BIOSTEC 2015, 8th International Joint Conference on Biomedical Engineering Systems and Technologies. ISBN 978-989758069-7. pp. 350-359, 2015
Publisher
SciTePress