  Despite a revolution in the pervasiveness of video cameras in our daily
lives, one of the most meaningful forms of nonverbal affective communication,
interpersonal eye gaze, i.e. eye gaze relative to a conversation partner, is
not available from common video. We introduce the Interpersonal-Calibrating
Eye-gaze Encoder (ICE), which automatically extracts interpersonal gaze from
video recordings without specialized hardware and without prior knowledge of
participant locations. Leveraging the intuition that individuals spend a large
portion of a conversation looking at each other enables the ICE dynamic
clustering algorithm to extract interpersonal gaze. We validate ICE in both
video chat using an objective metric with an infrared gaze tracker (F1=0.846,
N=8), as well as in face-to-face communication with expert-rated evaluations of
eye contact (r= 0.37, N=170). We then use ICE to analyze behavior in two
different, yet important affective communication domains: interrogation-based
deception detection, and communication skill assessment in speed dating. We
find that honest witnesses break interpersonal gaze contact and look down more
often than deceptive witnesses when answering questions (p=0.004, d=0.79). In
predicting expert communication skill ratings in speed dating videos, we
demonstrate that interpersonal gaze alone has more predictive power than facial
expressions.
