Laser Doppler vibrometry measures of physiological function: evaluation of biometric capabilities

  • Authors:

  • Mei Chen;Joseph A. O'Sullivan;Naveen Singla;Erik J. Sirevaag;Sean D. Kristjansson;Po-Hsiang Lai;Alan D. Kaplan;John W. Rohrbaugh

  • Affiliations:

  • Department of Electrical and Systems Engineering, Washington University in St. Louis, Saint Louis, MO;Department of Electrical and Systems Engineering, Washington University in St. Louis, Saint Louis, MO;Exegy Inc., Saint Louis, MO;Department of Psychiatry, Washington University in Saint Louis Medical School, Saint Louis, MO;Department of Psychiatry, Washington University in Saint Louis Medical School, Saint Louis, MO;Department of Electrical and Systems Engineering, Washington University in St. Louis, Saint Louis, MO;Department of Electrical and Systems Engineering, Washington University in St. Louis, Saint Louis, MO;Department of Psychiatry, Washington University in Saint Louis Medical School, Saint Louis, MO

  • Venue:
  • IEEE Transactions on Information Forensics and Security
  • Year:
  • 2010

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Abstract

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%.