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%.

中文翻译：

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基于肌电信号的个人识别方法研究。

随着互联网的发展，个人信息的安全性变得越来越重要。因此，已经引入了各种个人识别方法以确保人们的信息安全。诸如个人识别码（PIN）或识别标签（ID）之类的传统识别方法容易受到黑客的攻击。然后提出了一种利用人体独特的生理特征来识别用户信息的生物识别技术。但是，目前广泛使用的生物识别技术（例如人脸，指纹，虹膜和语音）也可以伪造和伪造。具有活体特征（例如肌电图（EMG）信号）的生物特征识别是实现活动性检测并防止欺骗攻击的好方法。然而，关于基于EMG信号的个人识别的研究很少。根据应用上下文，个人识别系统可以以识别模式或验证模式操作。在个人识别模式下，系统通过搜索数据库中所有用户的模板以查找匹配项来识别个人。在个人验证模式下，系统通过将捕获的功能与存储在系统数据库中的他或他自己的模板进行比较来验证一个人的身份。本文研究了基于EMG的个人识别方法和基于EMG的个人验证方法。首先，将Myo臂章放在21位受试者的右前臂（具体而言，是肱肱关节的高度）上，以张开手势收集表面EMG信号。然后，针对基于EMG的个人识别提出了两种不同的方法，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。通过对21个对象的实验表明，这两种方法的识别准确率分别达到99.206％和99.203％。然后在基于CWT和CNN的识别方法的基础上，采用转移学习算法解决了添加新数据时的模型更新问题。最后，提出了一种使用CWT和暹罗网络的基于EMG的个人验证方法。实验表明，该方法的验证精度可以达到99.285％。基于连续小波变换（CWT）和卷积神经网络（CNN）的个人识别方法。通过对21个对象的实验表明，这两种方法的识别准确率分别达到99.206％和99.203％。然后在基于CWT和CNN的识别方法的基础上，采用转移学习算法解决了添加新数据时的模型更新问题。最后，提出了一种使用CWT和暹罗网络的基于EMG的个人验证方法。实验表明，该方法的验证精度可以达到99.285％。基于连续小波变换（CWT）和卷积神经网络（CNN）的个人识别方法。通过对21个对象的实验表明，这两种方法的识别准确率分别达到99.206％和99.203％。然后在基于CWT和CNN的识别方法的基础上，采用转移学习算法解决了添加新数据时的模型更新问题。最后，提出了一种使用CWT和暹罗网络的基于EMG的个人验证方法。实验表明，该方法的验证精度可以达到99.285％。采用转移学习算法解决了新数据添加时的模型更新问题。最后，提出了一种使用CWT和暹罗网络的基于EMG的个人验证方法。实验表明，该方法的验证精度可以达到99.285％。采用转移学习算法解决了新数据添加时的模型更新问题。最后，提出了一种使用CWT和暹罗网络的基于EMG的个人验证方法。实验表明，该方法的验证精度可以达到99.285％。