Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition

2026-09-10

AI Summary

本文提出SV-GCN框架，通过单流多特征融合和时间不变性解决基于3D骨架的步态情感识别中的标注成本高、数据稀缺及泛化能力差的问题。

Abstract

3D skeleton-based gait emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data. This paper proposes SV-GCN, a single-stream multi-feature fusion framework with temporal invariance. We introduce intra-frame relative motion features to eliminate frame-rate sensitivity and embed heterogeneous cues at shallow layers, enabling early fusion without multi-stream complexity. For variable-length sequences, we design a global mask-guided valid-frame spatio-temporal graph convolution module, introducing frame-rate insensitivity for the first time in this domain. On the E-Gait dataset, our method achieves performance comparable to state-of-the-art while demonstrating strong generalization across varying sequence lengths and frame rates, offering a viable pathway for pre-training on large-scale skeleton-based action recognition datasets.

Problem

• 3D skeleton
• gait emotion recognition
• annotation costs
• data scarcity
• generalization

Innovation

• single-stream multi-feature fusion
• temporal invariance
• intra-frame relative motion features
• global mask-guided valid-frame spatio-temporal graph convolution