Gait Emotion Recognition Based on a Multi-scale Partitioning Directed Spatio-temporal Graph
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摘要: 为了有效获取节点之间在多尺度、远距离以及在时间和空间位置上的依赖关系，以提高对步态情绪识别精度，本文首先提出一种构建分区有向时空图的方法：使用所有帧节点进行构图，然后按区域有向连接。其次，提出一种多尺度分区聚合与分区融合的方法。通过图深度学习对图节点进行更新。并对相似节点特征进行融合。最后，提出一个多尺度分区有向自适应时空图卷积神经网络(MPDAST-GCN)方法。网络通过在时间维度上构建图，获取远距离帧节点特征，并自适应地学习每帧上的特征数据。MPDAST-GCN将输入数据分类成高兴、伤心、愤怒和平常4种情绪类型。并在发布的Emotion-Gait数据集上，相比于目前最先进的方法实现6%的精度提升。Abstract: To enhance the precision of gait emotion recognition by effectively capturing the dependencies between nodes at multiple scales, long distances, and temporal and spatial positions, a novel method comprising three parts is proposed in this paper. Firstly, a partitioned directed spatio-temporal graph construction method is proposed. It connects all frame nodes in a directed manner based on their regions. Secondly, a multi-scale partition aggregation and fusion method is proposed. This method updates the graph nodes using graph deep learning and fuses similar node features. Lastly, a Multi-scale Partition Directed Adaptive Spatio-Temporal Graph Convolutional Neural network (MPDAST-GCN) is proposed. It constructs a graph in the temporal dimension to obtain the features of distant frame nodes and learns the feature data adaptively on each frame. The MPDAST-GCN classifies input data into four emotion types: happy, sad, angry, and normal. Experimental results on the Emotion-Gait dataset demonstrate that the proposed method outperforms state-of-the-art methods by 6% in terms of accuracy.
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Key words:
- Gait emotion recognition /
- Emotion recognition /
- Graph deep learning
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表 2 是否使用分区聚合算法以及不同聚合尺度k对网络性能影响
Parameter Accuracy(%) k m Happy Sad Angry Normal MAP 不使用 1 1 96.8 86.0 87.0 70.8 85.1 1 1 98.9 89.4 86.4 70.0 86.2 使用 2 1 98.6 90.3 87.5 68.9 86.3 3 1 98.9 85.0 90.8 72.6 86.8 
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表 3 是否使用分区融合方法对网络性能影响
Parameter Accuracy(%) k m Happy Sad Angry Normal MAP 不使用 1 1 98.9 89.4 86.4 70.0 86.2 使用 1 1 97.8 92.2 88.8 74.3 88.3
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表 4 不同尺度下的图卷积块对网络性能影响
Parameter Accuracy(%) k m Happy Sad Angry Normal MAP MPDAST-GCN 1 1 97.8 92.2 88.8 74.3 88.3 1 2 96.8 92.6 91.0 76.0 89.1 1 3 97.4 90.0 91.8 79.0 89.6
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