  We present a new data-driven model and algorithm to identify the perceived
emotions of individuals based on their walking styles. Given an RGB video of an
individual walking, we extract his/her walking gait in the form of a series of
3D poses. Our goal is to exploit the gait features to classify the emotional
state of the human into one of four emotions: happy, sad, angry, or neutral.
Our perceived emotion recognition approach uses deep features learned via LSTM
on labeled emotion datasets. Furthermore, we combine these features with
affective features computed from gaits using posture and movement cues. These
features are classified using a Random Forest Classifier. We show that our
mapping between the combined feature space and the perceived emotional state
provides 80.07% accuracy in identifying the perceived emotions. In addition to
classifying discrete categories of emotions, our algorithm also predicts the
values of perceived valence and arousal from gaits. We also present an EWalk
(Emotion Walk) dataset that consists of videos of walking individuals with
gaits and labeled emotions. To the best of our knowledge, this is the first
gait-based model to identify perceived emotions from videos of walking
individuals.
