Methods › GER

Gait Emotion Recognition

GER

18 papers tagged archive 2025-07-28

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

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

PaperSourceSee Code · UttaranB127/STEP

Papers archive 2025-07-28

18 shown of 18, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 41 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Automatic Speech Recognition8
Automatic Speech Recognition (ASR)8
Speech Recognition8
speech-recognition8
Emotion Recognition3
Language Modelling3
Audio-Visual Speech Recognition2
Language Modeling2
Sentence2
Specificity2
Visual Speech Recognition2
Benchmarking1
Cloze Test1
Deep Learning1
Deep Reinforcement Learning1
Denoising1
Distributed Optimization1
Entity Retrieval1
Facial Emotion Recognition1
General Classification1

Usage over time archive 2025-07-28

Papers per year tagged with GER: 2019 to 2025, peak 8 8 0 2019: 1 paper 2019 2020: 1 paper 2020 2021: 1 paper 2021 2022: 2 papers 2022 2023: 3 papers 2023 2024: 8 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (18 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

The archive places this method in no collection.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections