Gait Emotion Recognition

Introduced by Bhattacharya et al. in STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

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 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.

Source: STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

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Task Papers Share
Graph Attention 1 20.00%
Language Modelling 1 20.00%
Sentence Embeddings 1 20.00%
Management 1 20.00%
General Classification 1 20.00%

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