基于多运动特征融合的微表情识别算法

Micro-Expression Recognition Algorithm Based on Multiple Motive Feature Fusion

直接使用原始微表情序列对微表情进行识别的效果一般,且已有的算法往往利用单一的特征图而没有对多种特征图进行融合来识别微表情。针对这些问题,提出一种新的微表情识别算法,该算法对多种运动特征图进行特征提取之后再进行融合,以获得更准确的识别结果。所提算法利用卷积神经网络(CNN)和长短期记忆(LSTM)网络结合的深度学习框架。在CASMEII微表情数据库上对不同算法进行测试。实验结果表明,与其他识别算法相比,所提算法取得了更加优良的效果。

In micro-expression recognition,directly using the original micro-expression sequence achieves sub-satisfactory results,and the existing algorithms often employ a single feature map rather than fusing multiple feature maps.To address these problems,this paper proposes a new micro-expression recognition algorithm that fuses motion feature maps after extracting the features to obtain more accurate recognition results.The proposed algorithm uses the fused deep learning framework between convolutional neural network(CNN)and long-and-short memory(LSTM)network.Different algorithms are evaluated on the CASMEII micro-expression database.Experimental results show that the proposed method performs better compared with other algorithms.