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

• URL: http://arxiv.org/abs/2609.11680v1
• Date: Thu, 10 Sep 2026 15:11:51 GMT
• Title: Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition
• Abstract summary: 3D skeleton-based gait emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data.<n>This paper proposes SV-GCN, a single-stream multi-feature fusion framework with temporal invariance.
• Score: 2.055138638073669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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