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

• URL: http://arxiv.org/abs/2405.13903v1
• Date: Wed, 22 May 2024 18:24:21 GMT
• Title: ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos
• Authors: Maria Luísa Lima, Willams de Lima Costa, Estefania Talavera Martinez, Veronica Teichrieb,
• Abstract summary: We propose a framework for emotion recognition through the analysis of gait. Our model is composed of a sequence of spatial-temporal Graph Convolutional Networks. We evaluate our proposed framework on the E-Gait dataset, composed of a total of 2177 samples.
• Score: 3.1489012476109854
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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