  We present a novel classifier network called STEP, to classify perceived
human emotion from gaits, based on a Spatial Temporal Graph Convolutional
Network (ST-GCN) architecture. Given an RGB video of an individual walking, our
formulation implicitly exploits the gait features to classify the emotional
state of the human into one of four emotions: happy, sad, angry, or neutral. We
use hundreds of annotated real-world gait videos and augment them with
thousands of annotated synthetic gaits generated using a novel generative
network called STEP-Gen, built on an ST-GCN based Conditional Variational
Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the
CVAE formulation of STEP-Gen to generate realistic gaits and improve the
classification accuracy of STEP. We also release a novel dataset (E-Gait),
which consists of $2,177$ human gaits annotated with perceived emotions along
with thousands of synthetic gaits. In practice, STEP can learn the affective
features and exhibits classification accuracy of 89% on E-Gait, which is 14 -
30% more accurate over prior methods.
