Abstract
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.
Highlights
Biometric systems are used to determine or verify an individual’s identity by measuring his/her physiological and behavioral characteristics that belong uniquely to an individual, i.e., biometric traits
We systematically investigated the performance of surface electromyogram (sEMG) signal in the person recognition via gesture recognition
The results of this study demonstrated the feasibility of sEMG signal as a biometric trait and its potential in enhancing the reliability of a biometric system
Summary
Biometric systems are used to determine or verify an individual’s identity by measuring his/her physiological and behavioral characteristics that belong uniquely to an individual, i.e., biometric traits. As biometric traits are unique and inherent to each individual, they are difficult to manipulate, share, or forget. Biometric traits greatly improve the reliability of the system in the recognition and authentication of individuals. The properties of biometric systems mainly depend on the specific traits they use. Systems based on them have already been embedded in our daily lives, such as mobile phones, laptops, and smart pads. These traits need to be exposed during recognition, providing the chance to be captured and synthetically generated.
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