Authors:
David Pereira Coutinho
1
;
Ana L. N. Fred
2
and
Mario A. T. Figueiredo
2
Affiliations:
1
Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações and Instituto Superior Técnico, Portugal
;
2
Instituto de Telecomunicações and Instituto Superior Técnico, Portugal
Keyword(s):
Biometrics, ECG, String matching, LZ77, Data compression.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Telecommunications
Abstract:
Conventional access control systems are typically based on a single time instant authentication. However, for high-security environments, continuous user verification is needed in order to robustly prevent fraudulent or unauthorized access. The electrocardiogram (ECG) is an emerging biometric modality with the following characteristics: (i) it does not require liveliness verification, (ii) there is strong evidence that it contains sufficient discriminative information to allow the identification of individuals from a large population, (iii) it allows continuous user verification. Recently, a string matching approach for ECG-based biometrics, using the Ziv-Merhav (ZM) cross parsing, was proposed. Building on previous work, and exploiting tools from data compression, this paper goes one step further, proposing a method for ECG-based continuous authentication. An adaptive way of using the ZM cross parsing is introduced. The use of the Lloyd-Max quantization is also introduced to improve
the results with the string matching approach for ECG-based biometrics. Results on one-lead ECG real data are presented, acquired during a concentration task, from 19 healthy individuals.
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