Educing Information: Interrogation - National Intelligence University

and potentially could interfere with the desired signal. Body movements such
as rocking or swaying could be potential countermeasures to this technology.
Sophisticated signal processing is required to extract the desired signal from
the noise. Potential solutions to these movement countermeasures are being
researched (Geisheimer and Greneker, 2000; Geisheimer and Greneker, 2001).
RVSM transmits at a frequency of 24.1 GHz, where skin reflects
approximately 73% of the wave; the rest of the energy is quickly dissipated in the
first several millimeters of the body. Given radiation safety concerns, the Federal
Communications Commission (FCC) radio frequency exposure regulations have
set a maximum permissible exposure level of 1 mW/cm 2 averaged over thirty
minutes for a transmission frequency of 24.1 GHz. These exposure limits can
be met at a distance of 12.85 cm from the RVSM transmitter (Geisheimer and
Greneker, 2000; Geisheimer and Greneker, 2001).
Because RVSM technology measures some of the same psychophysiological
responses as the polygraph, it is subject to the same criticisms (see Section 2.1).
Due to the lack of published results in the literature, it appears that researchers
have not yet explored this technology in the context of detecting deception. Given
this lack of scientific validation, RVSM is probably not ready to be deployed.
Facial Expressions
Human emotional responses can be recognized through facial expressions
(Ekman, 1994; Izard, 1994). According to Ekman (1972), a specific set of facial
expressions appears to be generated by the emotions of anger, disgust, fear,
happiness, sadness, surprise, and to a lesser degree contempt, embarrassment,
interest, pain, and shame. Ekman (1972) further states that these emotions are
universally generated and recognized across all cultures. Ekman and Friesen
(1978) developed the Facial Action Coding System (FACS) to measure all visible
facial muscle movements, not just those presumed to be related to emotion, as well
as head and eye movements. In approximately 100 to 300 hours, an individual
can learn to code facial expressions based on the characteristic pattern of bulges,
wrinkles, and movements for each facial Action Unit (AU), as well as to code the
intensity of each AU. Ekman and Friesen (1978) identified forty-four AUs that
can occur singly or in complex combinations.
A review of the literature shows that research into automatic recognition of
FACS AUs is limited (Tian et al., 2003). Deception detection experiments have
only coded FACS AUs manually (Ekman, 1985; Ekman et al., 1991; Frank and
Ekman, 1997). Despite this, the current state of the art is briefly reviewed here. In
2001, two teams (Carnegie Mellon University and the University of Pittsburgh —
referred to as CMU/Pitt, and the University of California, San Diego, and the
Salk Institute — referred to as UCSD/Salk) were tasked to quantitatively analyze
spontaneous facial expressions to estimate AUs (Cohen et al., 2001; Bartlett et
al., 2001). Both teams independently developed a non-intrusive automatic facial
expression recognition system capable of handling non-frontal pose, moderate
out-of-plane head motion, and moderate occlusion from head motion, eyeglasses,
gestures, talking, subtle facial actions, and rapid facial motion. It should be noted
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