Electromyograph and keystroke dynamics for spoof-resistant biometric authentication

Venugopalan, Shreyas; Juefei-Xu, Felix; Cowley, Benjamin R.; Savvides, Marios

doi:10.1109/cvprw.2015.7301326

Cited by 30 publications

(16 citation statements)

References 34 publications

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“…As little attention was received, there was limited literature on sEMG biometrics. In Belgacem et al (2015), Venugopalan et al (2015), and Cannan and Hu (2013), sEMG was used as the complementary information and combined with other physiological or behavioral data to strengthen the performance of user authentication. In Yamaba et al (2018a) and Yamaba et al (2019), the difference was observed in sEMG amplitudes from the same gesture made by different participants.…”

Section: Related Work and Our Contributionmentioning

confidence: 99%

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

Jiang

2020

Front. Bioeng. Biotechnol.

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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.

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Section: Related Work and Our Contributionmentioning

confidence: 99%

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

Jiang

2020

Front. Bioeng. Biotechnol.

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“…Especially in Czech legislation, there is no similar idea covered. Integrated electromyography (iEMG) in combination with Keystroke dynamics (KD) is used, for example, in order to increase access security to specific computer applications and/or information systems (6). By monitoring a larger set of probands we can get behavioural characteristics of the clerks, which could allow us to create a higher-level user authentication method.…”

Section: Discussionmentioning

confidence: 99%

A New Approach to the Evaluation of Local Muscular Load while Typing on a Keyboard

Schlenker¹,

Tichý²

2017

Cent Eur J Public Health

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The results indicated that all probands in our study exceeded the valid hygienic limits for the total amount of the small repetitive movements of the hands and forearms and the total amount of the keyboard typing movements. Most of the probands in our study also exceeded the valid hygienic limit for the highest average time-weighted value of the percent maximum voluntary contraction (%MVC). This implies that the keystroke dynamics method has a great potential to increase the accuracy of evaluation of local muscular load when using the keyboard and thus to improve the existing methodology for investigation of occupational diseases resulting from overload while working on the computer.

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“…Compared to other commonly used distance measurement such as ℓ 1 -norm, ℓ 2 -norm, NCD exhibits the best result [48][49][50][51][52][53][54][55]68,69]. The result of each algorithm is a similarity matrix whose entry SimM ij is the NCD between the feature vector of probe image i and target image j.…”

Section: Experimental Setup Overviewmentioning

confidence: 97%

Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition

Juefei-Xu

Savvides

2016

Pattern Recognition

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Electromyograph and keystroke dynamics for spoof-resistant biometric authentication

Cited by 30 publications

References 34 publications

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

A New Approach to the Evaluation of Local Muscular Load while Typing on a Keyboard

Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition

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