基于长短期记忆网络与特征融合的微表情识别算法

A micro-expression recognition algorithm based on long short-term memory networks and feature fusion

为了提高视频识别领域中微表情识别的准确率,提出了一种基于长短期记忆网络与特征融合的微表情识别算法。提取微表情图像的颜色特征和纹理特征,将所提取的空间特征传入卷积神经网络进行融合。设计了学习时域相关性的长短期记忆网络结构,将融合的特征集传入长短期记忆网络学习微表情的时域特征,将长短期记忆网络接入分类器网络识别出微表情的类标签。在两个公开的微表情识别数据集上完成了验证实验,结果显示算法实现了较好的微表情识别性能,在SMIC数据集和CASMEⅡ数据集上的准确率分别达到64.7%和65.8%.

In order to improve the accuracy of micro-expression recognition in video recognition field,a micro-expression recognition algorithm based on long short-term memory network and feature fusion is proposed.Both color features and texture features of micro expression images are extracted,and the selected spatial features are delivered to convolution neural networks for fusion.A new long short-term memory network is designed to learn temporal correlation between spatial features,the fused features are delivered to long short-term memory network to learn temporal features of micro expressions,the long short-term memory network is connected to classification network to recognize the class labels for each micro expression.Validation experiments are carried on two public micro expression recognition datasets,the results show that the proposed algorithm realizes better micro expression recognition performance,it realizes accuracy of 64.7%and 65.8%on SMIC dataset and CASMEⅡdataset,respectively.