Abstract
Electromyogram (EMG) signals cannot be forged and have the advantage of being able to change the registered data as they are characterized by the waveform, which varies depending on the gesture. In this paper, a two-step biometrics method was proposed using EMG signals based on a convolutional neural network–long short-term memory (CNN-LSTM) network. After preprocessing of the EMG signals, the time domain features and LSTM network were used to examine whether the gesture matched, and single biometrics was performed if the gesture matched. In single biometrics, EMG signals were converted into a two-dimensional spectrogram, and training and classification were performed through the CNN-LSTM network. Data fusion of the gesture recognition and single biometrics was performed in the form of an AND. The experiment used Ninapro EMG signal data as the proposed two-step biometrics method, and the results showed 83.91% gesture recognition performance and 99.17% single biometrics performance. In addition, the false acceptance rate (FAR) was observed to have been reduced by 64.7% through data fusion.
Highlights
Biometrics refers to the verification of identity using an individual’s behavioral and physical features
A two-step biometrics method using EMG signals based on the CNN-LSTM network was proposed
Noise in the EMG signal was removed using notch filter (NF) and band pass filter (BPF), and preprocessing was performed with root mean squarefeatures (RMS)
Summary
Biometrics refers to the verification of identity using an individual’s behavioral and physical features. Widely used biometrics include face, fingerprint, and iris recognition as physical features. Faces, fingerprints, and irises are bodily information without liveness that can be forged using 3D printers, lenses, and silicon, raising security concerns [1]. To compensate for this issue, biometrics studies using biosignals, which are a behavioral feature, are being conducted. There are two methods of measuring EMG signals: a needle electrode method and a surface electrode method. The needle electrode method measures an action potential occurring at a point in the muscle by inserting a needle electrode into a muscle. The surface electrode method measures an action potential by placing electrodes to the skin surface
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