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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:生物测定能力的评估。

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摘要

A novel approach for remotely sensing mechanical cardiovascular activity for use as a biometric marker is proposed. Laser Doppler Vibrometry (LDV) is employed to sense vibrations on the surface of the skin above the carotid artery related to arterial wall movements associated with the central blood pressure pulse. Carotid LDV signals are recorded using noncontact methods and the resulting unobtrusiveness is a major benefit of this technique. Several recognition methods are proposed that use the temporal and/or spectral information in the signal to assess biometric performance both on an intrasession basis, and on an intersession basis where LDV measurements were acquired from the same subjects after delays ranging from one week to six 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 of 0.5% for intrasession testing. However, performance degrades substantially for intersession 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 intersession equal-error rate of 6.3%.
机译:提出了一种新的方法,用于遥感机械心血管活动作为生物特征标记。激光多普勒振动测定法(LDV)用于检测颈动脉上方皮肤表面上与中央血压脉冲相关的动脉壁运动有关的振动。使用非接触方法记录颈动脉LDV信号,由此产生的不引人注意是该技术的主要优点。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,以在会期内和在会期基础上评估生物特征的性能,其中,在延迟一星期至六个月的时间后,从同一受试者获得了LDV测量值。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。对于此方法的身份验证测试对于会话内测试产生的错误率为0.5%。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。按照目前的做法,这种方法产生的会话间均等错误率为6.3%。

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