首页|Pyr-HGCN: Pyramid Hybrid Graph Convolutional Network for Gait Emotion Recognition
Pyr-HGCN: Pyramid Hybrid Graph Convolutional Network for Gait Emotion Recognition
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NETL
Springer Nature
Gait emotion recognition (GER) plays a crucial role in identifying human emotions。 Most previous methods apply Spatial-Temporal Graph Convolutional Networks (ST-GCN) to recognize emotions。 However， these methods suffer from two serious problems: (1) they ignore the fact that the similarity between emotions with the similar emotional intensity。 Consequently， fine-grained information from the low-layer network， which is essential for accurate emotion recognition， is lost。 (2) They ignore that the expression of emotion is a continuous process， that is， failing to model the temporal dimension effectively。 To address these issues， a novel Pyramid Hybrid Graph Convolutional Network (Pyr-HGCN) is proposed for GER。 Specifically， we first introduce and enhance the pyramid structure in GER to compensate for the missing fine-grained information of the ST-GCN structure。 Additionally， we design a novel Spatial-Temporal Hybrid Convolution (STHC) block， which can indirectly and simultaneously capture complex spatio-temporal correlations in long-term regions。 Extensive experiments and visualizations were performed on several benchmarks， with an accuracy improvement of 0。01 to 0。02 demonstrating the effectiveness of our approach against state-of-the-art competitors。
NETL
Springer Nature
