Individual Identification by Late Information Fusion of EmgCNN and EmgLSTM from Electromyogram Signals

Individual Identification by Late Information Fusion of EmgCNN and EmgLSTM from Electromyogram Signals

This paper is concerned with individual identification by late fusion of two-stream deep networks from Electromyogram (EMG) signals. EMG signal has more advantages on security compared to other biosignals exposed visually, such as the face, iris, and fingerprints, when used for biometrics, at least...

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Bibliographic Details
Main Authors: Yeong-Hyeon Byeon, Keun-Chang Kwak
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Sensors
Subjects:
individual identification
long short-term memory
convolutional neural network
electromyography
feature extraction
Online Access:https://www.mdpi.com/1424-8220/22/18/6770

Internet

https://www.mdpi.com/1424-8220/22/18/6770

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