Small movements of the skin overlying the carotid artery, arising from pressure pulse changes in the carotid during the cardiac cycle, can be detected using the method of Laser Doppler Vibrometry (LDV). Based on the premise that there is a high degree of individuality in cardiovascular function, the pulse-related movements were modeled for biometric use. Short time variations in the signal due to physiological factors are described and these variations are shown to be informative for identity verification and recognition. Hidden Markov models (HMMs) are used to exploit the dependence between the pulse signals over successive cardiac cycles. The resulting biometric classification performance confirms that the LDV signal contains information that is unique to the individual.
A novel approach using mechanical physiological activity as a biometric marker is described. Laser Doppler Vibrometry
is used to sense activity in the region of the carotid artery, related to arterial wall movements associated
with the central blood pressure pulse. The non-contact basis of the LDV method has several potential benefits in
terms of the associated non-intrusiveness. Several 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 on an intersession
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 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 in
multi-session data training model. As currently implemented, this approach yields an inter-session equal-error
rate of 9%.
There is a critical need to conduct operational interviews in a wide range of interview and assessment situations, including conventional structured interviews as well as cases in which subjects are unconstrained. Current progress of three advanced prototype instrument development projects looking at non-contact sensing of human physiology to determine the veracity of human communications are presented. These include: 1) Thermal Facial Screening (TFS); 2) Turnkey Remote Assessment of Concealed Knowledge using Eye movement Recordings (TRACKER); and 3) Laser Doppler Vibrometry (LDV). Signals are measured with superior technical quality, in comparison to those obtained with conventional contact methods. Depending on the operational need and the specific context, these instruments can be used as stand-alone techniques or integrated into a multi-modal evaluation of human credibility. Thus, a comprehensive assessment using multiple physiological response systems is possible. A description each technique, the current state of these research efforts, and an overview of the potential for each of these emerging technologies will be provided.
Law Enforcement personnel are faced with new challenges to rapidly assess the credibility of statements made by individuals in airports, border crossings, and a variety of environments not conducive to interviews. New technologies may offer assistance to law enforcement personnel in the interview and interrogation process. Additionally, homeland defense against terrorism challenges scientists to develop new methods of assessing truthfulness and credibility in humans. Current findings of four advanced research projects looking at emerging technologies in the credibility assessment are presented for discussion. This paper will discuss research efforts on four emerging technologies now underway at DoDPI and other institutions. These include: (1) Thermal Image Analysis (TIA); (2) Laser Doppler Vibrometry (LDV); (3) Eye Movement based Memory Assessment (EMMA); and (4) functional Magnetic Resonance Imaging (fMRI). A description each technique, the current state of these research efforts, and an overview of the potential for each of these emerging technologies will be provided.