论文标题
基于肌电图的开放访问数据集多代码生物特征验证
Open Access Dataset for Electromyography based Multi-code Biometric Authentication
论文作者
论文摘要
最近，已经提出了表面肌电图（EMG）作为一种新型的生物特征性状，以解决当前生物识别技术的一些关键局限性，例如欺骗和livesing。 EMG信号具有独特的特征：对个体（生物识别技术）的本质不同，并且可以自定义以实现多长度代码或密码（例如，通过执行不同的手势）。但是，当前基于EMG的生物识别研究具有两个关键局限性：1）与其他更具成熟的生物特征性状相比，一个小的主题库，以及2）限于单时间或单日数据集。在这项研究中，前臂和腕部EMG数据是从三个不同天的43个参与者中收集的，在他们进行静态手和腕手势时，他们的分离很长。多天的生物识别认证导致前臂设置的中位数为0.017，腕部设置为0.025，与成熟的生物识别性状相当，表明在多天内表明性能一致。提出的大型多日数据集和发现可以促进基于EMG的生物识别技术和其他基于手势识别的应用程序的进一步研究。
Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize multi-length codes or passwords (for example, by performing different gestures). However, current EMG-based biometric research has two critical limitations: 1) a small subject pool, compared to other more established biometric traits, and 2) limited to single-session or single-day data sets. In this study, forearm and wrist EMG data were collected from 43 participants over three different days with long separation while they performed static hand and wrist gestures. The multi-day biometric authentication resulted in a median EER of 0.017 for the forearm setup and 0.025 for the wrist setup, comparable to well-established biometric traits suggesting consistent performance over multiple days. The presented large-sample multi-day data set and findings could facilitate further research on EMG-based biometrics and other gesture recognition-based applications.
