MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition

TL;DR

This paper introduces MSA-GCN, a novel multiscale adaptive graph convolution network that effectively captures global emotional states in gait sequences by dynamically adjusting receptive fields and aggregating multiscale information.

Contribution

The work proposes a new MSA-GCN model with adaptive spatial-temporal convolution and a cross-scale fusion mechanism for improved gait emotion recognition.

Findings

Achieves state-of-the-art performance on two public datasets

Improves mAP by 2% over previous methods

Demonstrates effectiveness of multiscale adaptive features

Abstract

Gait emotion recognition plays a crucial role in the intelligent system. Most of the existing methods recognize emotions by focusing on local actions over time. However, they ignore that the effective distances of different emotions in the time domain are different, and the local actions during walking are quite similar. Thus, emotions should be represented by global states instead of indirect local actions. To address these issues, a novel Multi Scale Adaptive Graph Convolution Network (MSA-GCN) is presented in this work through constructing dynamic temporal receptive fields and designing multiscale information aggregation to recognize emotions. In our model, a adaptive selective spatial-temporal graph convolution is designed to select the convolution kernel dynamically to obtain the soft spatio-temporal features of different emotions. Moreover, a Cross-Scale mapping Fusion Mechanism (CSFM) is designed to construct an adaptive adjacency matrix to enhance information interaction and reduce redundancy. Compared with previous state-of-the-art methods, the proposed method achieves the best performance on two public datasets, improving the mAP by 2\%. We also conduct extensive ablations studies to show the effectiveness of different components in our methods.