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This content will become publicly available on November 1, 2022

Title: Deep Learning-based User Authentication with Surface EMG Images of Hand Gestures
User authentication is an important security mechanism to prevent unauthorized accesses to systems or devices. In this paper, we propose a new user authentication method based on surface electromyogram (sEMG) images of hand gestures and deep anomaly detection. Multi-channel sEMG signals acquired during the user performing a hand gesture are converted into sEMG images which are used as the input of a deep anomaly detection model to classify the user as client or imposter. The performance of different sEMG image generation methods in three authentication test scenarios are investigated by using a public hand gesture sEMG dataset. Our experimental results demonstrate the viability of the proposed method for user authentication.
Authors:
; ;
Award ID(s):
1757207
Publication Date:
NSF-PAR ID:
10312480
Journal Name:
Pu2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Sponsoring Org:
National Science Foundation
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