Perceiving human emotions is crucial in the realm of affective computing. As a nonverbal biological feature, gait plays a significant role in this field, owing to its resistance to manipulation or replication. In this paper, we propose a gait-based emotion perception framework called Dependency-Difference Gait (DDG), which can extract emotional features from gait patterns comprehensively and efficiently. We also introduce a method of spatial–temporal difference representation, which constructs the static spatial difference information within frames and dynamic temporal difference information between frames. We abstract these details as difference information and fuse them with the dependency information extracted from the original sequence. Our approach not only breaks the limitations of hand-crafted features, but also enables the extraction of a broader spectrum of emotional features. Additionally, we present the Emotional Information Attention (EIA) mechanism, allowing DDG to focus on key joints and frames based on the quantity of emotional information. Experimental and visualization results substantiate the effectiveness of the DDG and EIA. In the quality analysis, we find that selecting a few number of joints with a substantial amount of emotional information is beneficial for emotion classification. However, selecting a few frames can disrupt the temporal structure of the sequence, resulting in suboptimal performance.

感知人类情感在情感计算领域至关重要。作为一种非语言生物学特征，步态由于其对操纵或复制的抵抗力而在该领域发挥着重要作用。在本文中，我们提出了一种基于步态的情感感知框架，称为依赖差异步态（DDG），它可以全面有效地从步态模式中提取情感特征。我们还介绍了一种时空差异表示方法，该方法构建帧内的静态空间差异信息和帧之间的动态时间差异信息。我们将这些细节抽象为差异信息，并将它们与从原始序列中提取的依赖信息融合。我们的方法不仅打破了手工制作特征的限制，而且还能够提取更广泛的情感特征。此外，我们提出了情感信息注意力（EIA）机制，使DDG能够根据情感信息的数量来关注关键关节和框架。实验和可视化结果证实了 DDG 和 EIA 的有效性。在质量分析中，我们发现选择少量具有大量情感信息的关节有利于情感分类。然而，选择几个帧可能会破坏序列的时间结构，从而导致性能不佳。实验和可视化结果证实了 DDG 和 EIA 的有效性。在质量分析中，我们发现选择少量具有大量情感信息的关节有利于情感分类。然而，选择几个帧可能会破坏序列的时间结构，从而导致性能不佳。实验和可视化结果证实了 DDG 和 EIA 的有效性。在质量分析中，我们发现选择少量具有大量情感信息的关节有利于情感分类。然而，选择几个帧可能会破坏序列的时间结构，从而导致性能不佳。