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Despite a revolution in the pervasiveness of video cameras in our dailylives, one of the most meaningful forms of nonverbal affective communication,interpersonal eye gaze, i.e. eye gaze relative to a conversation partner, isnot available from common video. We introduce the Interpersonal-CalibratingEye-gaze Encoder (ICE), which automatically extracts interpersonal gaze fromvideo recordings without specialized hardware and without prior knowledge ofparticipant locations. Leveraging the intuition that individuals spend a largeportion of a conversation looking at each other enables the ICE dynamicclustering algorithm to extract interpersonal gaze. We validate ICE in bothvideo 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 ofeye contact (r= 0.37, N=170). We then use ICE to analyze behavior in twodifferent, yet important affective communication domains: interrogation-baseddeception detection, and communication skill assessment in speed dating. Wefind that honest witnesses break interpersonal gaze contact and look down moreoften than deceptive witnesses when answering questions (p=0.004, d=0.79). Inpredicting expert communication skill ratings in speed dating videos, wedemonstrate that interpersonal gaze alone has more predictive power than facialexpressions.

Are you really looking at me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze from Conventional Video