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首页> 外文学位 >Laser Doppler Vibrometry measures of physiological function: Evaluation of biometric capabilities.
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Laser Doppler Vibrometry measures of physiological function: Evaluation of biometric capabilities.

机译：生理功能的激光多普勒振动测量：生物特征评估。

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摘要

Due to increasing requirements for security, the application and importance of biometrics is growing at a rapid pace. Biometrics is the science of using physiological or behavioral characteristics to determine or verify attributes of a person, including identity. Fingerprints, iris scans, face images, and retina scans are examples of measurements of physiological characteristics that have been proposed and are being used as biometrics. Gait and signature are two primarily behavioral characteristics that have been explored for their use as biometrics. More biometric systems are under development as current biometric technologies satisfy those desirable attributes with mixed success. In biometric recognition, two key properties for useful biometrics are their ability to distinguish among individuals and their stability over time.;A novel approach of measuring carotid pulse signals via a laser Doppler vibrometer remotely is proposed. Laser Doppler Vibrometry (LDV) is used to sense vibration on the surface of the skin above the carotid artery. This motion is related to arterial wall movements associated with the central blood pressure pulse. The non-contact basis of the LDV method has several potential benefits related to non-invasiveness. To enhance the technical quality of the laser signal during this developmental effort, a small patch (1 cm 2) of reflective tape was affixed to the recording site.;The biometric capabilities of Laser Doppler Vibrometry (LDV) signals are evaluated. Several recognition methods are proposed that use the temporal and/or spectral information in the signal to assess biometric performance both on an intra-session basis, and an inter-session basis involving testing repeated after delays of 1 week to 6 months. A waveform decomposition method that utilizes principal component analysis is used to model the signal in the time domain. Authentication testing for this approach produces an equal-error rate (EER) of 0.5% for intra-session testing. However, performance degrades substantially for inter-session testing, requiring a more robust approach to modeling. Improved performance is obtained using techniques based on time-frequency decomposition, incorporating a method for extracting informative components. Biometric fusion methods including data fusion and information fusion are applied to train models using data from multiple sessions. As currently implemented, this approach yields an inter-session EER of 6.3%.;LDV biometric performance under moderate exercise is tested. A protocol is set up to produce changes in heart rate by physical exercise. Spectrogram based approaches are applied with an EER of 3.6% for inter-state tests, indicating that the LDV pulse signal is stable after moderate physical exercise. The performance degrades during exercise, but improves within 30 seconds as the heart rate recovers during the rest period. The results suggest that the variability caused by heart rate fluctuations and respiration changes decreases within a short time.
机译：由于对安全性的要求不断提高，生物识别技术的应用和重要性正在迅速增长。生物识别是一门使用生理或行为特征来确定或验证人的属性（包括身份）的科学。指纹，虹膜扫描，面部图像和视网膜扫描是已提出并被用作生物特征的生理特征的测量示例。步态和签名是两个主要的行为特征，已被探索用作生物特征识别。随着当前的生物识别技术满足那些可取的特性并取得成功，更多的生物识别系统正在开发中。在生物特征识别中，有用的生物特征的两个关键特性是它们在个体之间的区分能力和随时间的稳定性。提出了一种通过激光多普勒振动计远程测量颈动脉脉冲信号的新方法。激光多普勒振动测定法（LDV）用于检测颈动脉上方皮肤表面的振动。该运动与与中央血压脉冲相关的动脉壁运动有关。 LDV方法的非接触基础具有与无创性相关的若干潜在好处。为了在此开发过程中提高激光信号的技术质量，将一小片（1厘米2）的反射带粘贴到记录位置。评估了激光多普勒振动计（LDV）信号的生物识别能力。提出了几种识别方法，它们使用信号中的时间和/或频谱信息来评估会话期间内和会话间基础上的生物统计性能，其中涉及延迟1周至6个月后重复进行的测试。使用主成分分析的波形分解方法用于在时域中对信号建模。针对此方法的身份验证测试对于会话内测试产生0.5％的均等错误率（EER）。但是，对于会话间测试，性能会大大降低，这需要一种更可靠的建模方法。使用基于时频分解的技术，结合提取信息成分的方法，可以提高性能。包括数据融合和信息融合在内的生物特征融合方法被用于使用来自多个会话的数据来训练模型。按照目前的实施方式，这种方法在会议期间的EER为6.3％。制定协议以通过体育锻炼产生心率变化。应用基于频谱图的方法进行状态间测试时的EER为3.6％，这表明LDV脉冲信号在进行适度的体育锻炼后是稳定的。运动期间性能下降，但在休息期间心率恢复后30秒内性能会改善。结果表明，由心率波动和呼吸变化引起的变异性在短时间内降低。

著录项

  • 作者

    Chen, Mei.;

  • 作者单位

    Washington University in St. Louis.;

  • 授予单位 Washington University in St. Louis.;
  • 学科 Engineering Biomedical.;Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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