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User authentication using on-line signature and speech.

机译:使用在线签名和语音的用户身份验证。

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摘要

Ensuring the security of medical records is becoming an increasingly important problem as modern technology is integrated into existing medical services. As a consequence of the adoption of electronic medical records in the health care sector, it is becoming more and more common for a health professional to edit and view a patient's record using a tablet PC. In order to protect the patient's privacy, as required by governmental regulations in the United States, a secure authentication system to access patient records must be used. Biometric-based access is capable of providing the necessary security. On-line signature and voice modalities seem to be the most convenient for the users in such authentication systems because a tablet PC comes equipped with the associated sensors/hardware. This thesis analyzes the performance of combining the use of on-line signature and voice biometrics in order to perform robust user authentication. Signatures are verified using the dynamic programming technique of string matching. Voice is verified using a commercial, off the shelf, software development kit. In order to improve the authentication performance, we combine information from both on-line signature and voice biometrics. After suitable normalization of scores, fusion is performed at the matching score level. A prototype bimodal authentication system for accessing medical records has been designed and evaluated on a truly multimodal database of 100 users, resulting in an average equal error rate of 0.72%.
机译:随着现代技术被集成到现有的医疗服务中,确保医疗记录的安全性变得越来越重要。由于医疗保健领域采用了电子病历,因此医疗保健专业人员使用平板电脑编辑和查看患者病历变得越来越普遍。为了保护患者的隐私,按照美国政府法规的要求,必须使用安全的身份验证系统来访问患者记录。基于生物特征的访问能够提供必要的安全性。在这种身份验证系统中,对于用户而言,在线签名和语音模式似乎是最方便的,因为Tablet PC配备了相关的传感器/硬件。本文分析了结合使用在线签名和语音生物识别技术以执行可靠的用户身份验证的性能。使用字符串匹配的动态编程技术来验证签名。语音已使用现成的商业软件开发套件进行了验证。为了提高身份验证性能,我们结合了在线签名和语音生物识别信息。在对分数进行适当的归一化之后,以匹配的分数级别执行融合。已经设计出了用于访问病历的双峰身份验证原型系统,并在一个由100个用户组成的真正多峰数据库中对其进行了评估,平均错误率平均为0.72%。

著录项

  • 作者

    Krawczyk, Stephen.;

  • 作者单位

    Michigan State University.;

  • 授予单位 Michigan State University.;
  • 学科 Computer Science.
  • 学位 M.S.
  • 年度 2005
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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