# ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos

arXiv: https://arxiv.org/abs/2405.13903 · HTML: https://mlsanity.com/paper/2405.13903/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 7 of 7)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [PYSKL: Towards Good Practices for Skeleton Action Recognition](https://mlsanity.com/paper/2205.09443/) (2022)
- [STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits](https://mlsanity.com/paper/1910.12906/) (2019)
- [ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation](https://mlsanity.com/paper/2003.01062/) (2020)
- [Identifying Emotions from Walking using Affective and Deep Features](https://mlsanity.com/paper/1906.11884/) (2019)
- [Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey](https://mlsanity.com/paper/2107.05989/) (2021)
- [Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices](https://mlsanity.com/paper/1707.07180/) (2017)

## Built on by (most cited first, 3 of 3)

- [Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context](https://mlsanity.com/paper/2507.12889/) (2025)
- [CGTGait: Collaborative Graph and Transformer for Gait Emotion Recognition](https://mlsanity.com/paper/2509.16623/) (2025)
- [Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition](https://mlsanity.com/paper/2609.11680/) (2026)

## Abstract (arXiv)

Emotion recognition is relevant for human behaviour understanding, where facial expression and speech recognition have been widely explored by the computer vision community. Literature in the field of behavioural psychology indicates that gait, described as the way a person walks, is an additional indicator of emotions. In this work, we propose a deep framework for emotion recognition through the analysis of gait. More specifically, our model is composed of a sequence of spatial-temporal Graph Convolutional Networks that produce a robust skeleton-based representation for the task of emotion classification. We evaluate our proposed framework on the E-Gait dataset, composed of a total of 2177 samples. The results obtained represent an improvement of approximately 5% in accuracy compared to the state of the art. In addition, during training we observed a faster convergence of our model compared to the state-of-the-art methodologies.