Biometric Authentication based on EMG Signals of Speech

Khan, Muhammad Umar; Choudry, Zainoor Ahmad; Aziz, Sumair; Naqvi, Syed Zohaib Hassan; Aymin, Afeefa; Imtiaz, Muhammad

doi:10.1109/icecce49384.2020.9179354

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…Most existing works using EMG signals have been conducted on gesture recognition and biometrics from hand muscles measured while performing hand gestures [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. These works include studies using EMG signals of the lower body when a subject is walking [14] and those using EMG signals of the mouth muscles when talking [15,18]. In addition, biometrics studies using multimodal information are being conducted to compensate for the shortcomings of existing biometrics [3,[19][20][21].…”

Section: Related Workmentioning

confidence: 99%

“…Noise in EMG signals was eliminated with a 5 Hz HPF and a 60 Hz NF, utilizing eleven time domain features and a one-class support vector machine (OCSVM) and local outlier factor (LOF). Khan [18] studied biometrics using speech EMG. Khan used empirical mode decomposition (EMD) to eliminate noise in signals and set areas of interest and utilize time-frequency domain features.…”

Section: Biometrics Using Emg Signalmentioning

confidence: 99%

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Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

Kim

Pan

2021

Applied Sciences

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Biometrics Using Emg Signalmentioning

confidence: 99%

Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

Kim

Pan

2021

Applied Sciences

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“…2 presents the measurement procedure for sEMG signals. Existing studies on personal recognition using sEMG had extracted features from sEMG [10][11][12][13][14][15] or increased feature data by overlapping windows [16][17][18]. However, overlapping windows within the same data can trigger overfitting, which degrades the recognition performance owing to data generalization [19].…”

Section: Introductionmentioning

confidence: 99%

“…Empirical mode decomposition can increase feature data without data overlapping by decomposing data into physically meaningful components. Existing studies adopted sEMG-based EMD to remove noise from the sEMG, rather than increasing feature data [9,15,20,21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security

Kim¹,

Kim²,

Kim³

et al. 2022

Computers, Materials &Amp; Continua

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As fifth generation technology standard (5G) technology develops, the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing. The existing personal recognition method used for granting permission is a password-based method, which causes security problems. Therefore, personal recognition studies using bio-signals are being conducted as a method to access control to devices. Among bio-signal, surface electromyogram (sEMG) can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation. Furthermore, as an advantage, sEMG can be conveniently measured from arms and legs. This paper proposes a personal recognition method using sEMG, based on a multi-stream convolutional neural network (CNN). The proposed method decomposes sEMG signals into intrinsic mode functions (IMF) using empirical mode decomposition (EMD) and transforms each IMF into a spectrogram. Personal recognition is performed by analyzing time-frequency features from the spectrogram transformed into multi-stream CNN. The database (DB) adopted in this paper is the Ninapro DB, which is a benchmark EMG DB. The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%, compared with the singlestream CNN using the spectrogram of raw sEMG.

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“…In contrast, the EMG-based authentication systems are more suitable because they are not influenced by the surrounding noise or the health of the user. Therefore, authentication studies using EMG signals have been actively conducted in the recent years [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

et al. 2021

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Personal authentication systems employing biometrics are attracting increasing attention owing to their relatively high security compared to existing authentication systems. In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication. In previous studies, wet electrodes were attached to the skin for measuring biosignals. Wet electrodes contain adhesives and conductive gels, leading to problems such as skin rash and signal-quality deterioration in long-term measurements. The miniaturized wearable EMG system developed in this study comprised flexible dry electrodes attached to the watch strap, enabling EMG measurements without additional electrodes. In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector machine (SVM) model was implemented. The model using cubic SVM achieved the highest personal authentication rate of 87.1%. We confirmed the possibility of implementing a wearable authentication system by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.

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Biometric Authentication based on EMG Signals of Speech

Cited by 12 publications

References 12 publications

Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security

Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

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