  We demonstrate the utility of elementary head-motion units termed kinemes for
behavioral analytics to predict personality and interview traits. Transforming
head-motion patterns into a sequence of kinemes facilitates discovery of latent
temporal signatures characterizing the targeted traits, thereby enabling both
efficient and explainable trait prediction. Utilizing Kinemes and Facial Action
Coding System (FACS) features to predict (a) OCEAN personality traits on the
First Impressions Candidate Screening videos, and (b) Interview traits on the
MIT dataset, we note that: (1) A Long-Short Term Memory (LSTM) network trained
with kineme sequences performs better than or similar to a Convolutional Neural
Network (CNN) trained with facial images; (2) Accurate predictions and
explanations are achieved on combining FACS action units (AUs) with kinemes,
and (3) Prediction performance is affected by the time-length over which head
and facial movements are observed.
