Lu Lijing, Mao Jingna, Wang Wuqi, Ding Guangxin, Zhang Zhiwei
IEEE Trans Biomed Circuits Syst. 2020 Aug;14(4):681-691. doi: 10.1109/TBCAS.2020.3005148. Epub 2020 Jun 26.
With the increasing development of internet, the security of personal information becomes more and more important. Thus, variety of personal recognition methods have been introduced to ensure persons' information security. Traditional recognition methods such as Personal Identification Number (PIN), or Identification tag (ID) are vulnerable to hackers. Then the biometric technology, which uses the unique physiological characteristics of human body to identify user information has been proposed. But the biometrics widely used at present such as human face, fingerprint, iris, and voice can also be forged and falsified. The biometric with living body features such as electromyography (EMG) signal is a good method to achieve aliveness detection and prevent the spoofing attacks. However, there are few studies on personal recognition based on EMG signal. According to the application context, personal recognition system may operate either in identification mode or verification mode. In the personal identification mode, the system recognizes an individual by searching the templates of all the users in the database for a match. While in the personal verification mode, the system validates a person's identity by comparing the captured features with her or his own template(s) stored in the system database. In this paper, both EMG-based personal identification method and EMG-based personal verification method are investigated. First, the Myo armband is placed on the right forearm (specifically, the height of the radiohumeral joint) of 21 subjects to collect the surface EMG signal under hand-open gesture. Then, two different methods are proposed for EMG-based personal identification, i.e., personal identification method based on Discrete Wavelet Transform (DWT) and ExtraTreesClassifier, and personal identification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN). Experiments with 21 subjects show that the identification accuracy of this two methods can achieve 99.206% and 99.203% respectively. Then based on the identification method using CWT and CNN, transfer learning algorithm is adopted to solve the model update problem when new data is added. Finally, an EMG-based personal verification method using CWT and siamese networks is proposed. Experiments show that the verification accuracy of this method can achieve 99.285%.
随着互联网的不断发展，个人信息安全变得越来越重要。因此，人们引入了各种各样的个人识别方法来确保个人信息安全。传统的识别方法，如个人识别码（PIN）或身份识别标签（ID），容易受到黑客攻击。于是，提出了利用人体独特生理特征来识别用户信息的生物识别技术。但目前广泛使用的生物识别技术，如人脸、指纹、虹膜和语音，也可能被伪造和假冒。具有活体特征的生物识别技术，如肌电图（EMG）信号，是实现活体检测和防止欺骗攻击的一种好方法。然而，基于EMG信号的个人识别研究很少。根据应用场景，个人识别系统可以在识别模式或验证模式下运行。在个人识别模式下，系统通过在数据库中搜索所有用户的模板来识别个体，以找到匹配项。而在个人验证模式下，系统通过将捕获的特征与存储在系统数据库中的用户自己的模板进行比较来验证个人身份。在本文中，研究了基于EMG的个人识别方法和基于EMG的个人验证方法。首先，将Myo臂带放置在21名受试者的右前臂（具体来说，是桡肱关节的高度）上，以收集手部张开手势下的表面肌电信号。然后，提出了两种不同的基于EMG的个人识别方法，即基于离散小波变换（DWT）和ExtraTreesClassifier的个人识别方法，以及基于连续小波变换（CWT）和卷积神经网络（CNN）的个人识别方法。对21名受试者进行的实验表明，这两种方法的识别准确率分别可以达到99.206%和99.203%。然后，基于使用CWT和CNN的识别方法，采用迁移学习算法来解决添加新数据时的模型更新问题。最后，提出了一种使用CWT和连体网络的基于EMG的个人验证方法。实验表明，该方法的验证准确率可以达到99.285%。
