GER
Gait Emotion Recognition
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](https://paperswithcode.com/method/gcn)) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the perceived emotion of the human into one of four emotions: happy, sad, angry, or neutral. We train STEP on annotated real-world gait videos, augmented with 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 4,227 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 88\% on E-Gait, which is 14--30\% more accurate over prior methods.
STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
https://paperswithcode.com/paper/step-spatial-temporal-graph-convolutional
STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits