基于多尺度分区有向时空图的步态情绪识别

Gait Emotion Recognition Based on a Multi-scale Partitioning Directed Spatio-temporal Graph

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。

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.