  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.
