Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition.
biometrics
gesture recognition
surface electromyogram
user identification
user verification
Journal
Frontiers in bioengineering and biotechnology
ISSN: 2296-4185
Titre abrégé: Front Bioeng Biotechnol
Pays: Switzerland
ID NLM: 101632513
Informations de publication
Date de publication:
2020
2020
Historique:
received:
11
09
2019
accepted:
22
01
2020
entrez:
3
3
2020
pubmed:
3
3
2020
medline:
3
3
2020
Statut:
epublish
Résumé
Electrical biosignals are favored as biometric traits due to their hidden nature and allowing for liveness detection. This study explored the feasibility of surface electromyogram (sEMG), the electrical manifestation of muscle activities, as a biometric trait. The accurate gesture recognition from sEMG provided a unique advantage over two traditional electrical biosignal traits, electrocardiogram (ECG), and electroencephalogram (EEG), enabling users to customize their own gesture codes. The performance of 16 static wrist and hand gestures was systematically investigated in two identity management modes: verification and identification. The results showed that for a single fixed gesture, using only 0.8-second data, the averaged equal error rate (EER) for verification was 3.5%, and the averaged rank-1 for identification was 90.3%, both comparable to the reported performance of ECG and EEG. The function of customizing gesture code could further improve the verification performance to 1.1% EER. This work demonstrated the potential and effectiveness of sEMG as a biometric trait in user verification and identification, beneficial for the design of future biometric systems.
Identifiants
pubmed: 32117937
doi: 10.3389/fbioe.2020.00058
pmc: PMC7033497
doi:
Types de publication
Journal Article
Langues
eng
Pagination
58Informations de copyright
Copyright © 2020 He and Jiang.
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