Identifying Emotions from Walking using Affective and Deep Features

Understand

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.