Recent advances in biometric research have established surface electromyogram (sEMG) as a potential spoof-free solution to address some key limitations in current biometric traits. The nature of sEMG signals provide a unique dual-mode security: sEMGs have individual-specific characteristics (biometrics), and users can customize and change gestures just like passcodes. Such security also facilitates the use of code sequences (multicode) to further enhance the security. In this study, three levels of fusion, score, rank, and decision were investigated for two biometric applications, verification and identification. This study involved 24 subjects performing 16 hand/finger gestures, and code sequences with varying codelengths were generated. The performance of the verification and identification system was analyzed for varying codelength (M: 1–6) and rank (K: 1–4) to determine the best fusion scheme and desirable parameter values for a multicode sEMG biometric system. The results showed that the decision-level fusion scheme using a weighted majority voting resulted in an average equal error rate of 0.6% for the verification system when M = 4. For the identification system, the score-level fusion scheme with score normalization based on fitting a Weibull distribution resulted in a minimum false rejection rate of 0.01% and false acceptance rate of 4.7% using a combination of K = 2 and M = 4. The results also suggested that the parameters M and K could be adjusted based on the number of users in the database to facilitate optimal performance. In summary, a multicode sEMG biometric system was developed to provide improved dual-mode security based on the personalized codes and biometric traits of individuals, with the combination of enhanced security and flexibility.

中文翻译：

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基于多码肌电图的验证和识别生物识别的评分、排名和决策级融合策略


生物识别研究的最新进展已将表面肌电图 (sEMG) 确定为一种潜在的无欺骗解决方案，以解决当前生物识别特征的一些关键限制。 sEMG 信号的性质提供了独特的双模式安全性：sEMG 具有个人特定特征（生物识别），用户可以像密码一样自定义和更改手势。这种安全性还便于使用代码序列（多代码）来进一步增强安全性。在本研究中，针对验证和识别这两种生物识别应用，研究了融合、评分、排名和决策三个级别。这项研究涉及 24 名受试者，他们执行 16 个手/手指手势，并生成了不同代码长度的代码序列。针对不同的码长（M：1-6）和等级（K：1-4）分析验证和识别系统的性能，以确定多码 sEMG 生物识别系统的最佳融合方案和理想的参数值。结果表明，当M=4时，采用加权多数投票的决策级融合方案使验证系统的平均等错误率为0.6%。对于识别系统，采用基于分数归一化的分数级融合方案。使用 K = 2 和 M = 4 的组合拟合威布尔分布，得到的最小错误拒绝率为 0.01%，错误接受率为 4.7%。结果还表明，参数 M 和 K 可以根据数量进行调整数据库中的用户数以促进最佳性能。总之，开发了一种多码表面肌电生物识别系统，基于个人的个性化代码和生物特征，提供改进的双模式安全性，同时增强了安全性和灵活性。