基于卷积注意力模块和双通道网络的微表情识别算法

微表情是一种人类在试图隐藏自己真实情感时作出的面部动作,具有持续时间短、幅度小的典型特点.针对微表情识别难度大、识别效果不理想的问题,提出一种基于卷积注意力模块(CBAM)和双通道网络(DPN)的微表情识别算法——CBAM-DPN.首先,进行典型微表情数据集的数据融合;然后,分析序列帧中像素的变化值以确定顶点帧位置,再...

Micro-expression recognition algorithm based on convolutional block attention module and dual path networks

Micro-expression is a facial movement that humans make when they are trying to hide their true emotions. It has the typical characteristics of short duration and small amplitude. Concerning the problems of the difficulty in recognition and the unsatisfactory recognition effect of micro-expression, a micro-expression recognition algorithm based on Convolutional Block Attention Module (CBAM) and Dual Path Networks (DPN), namely CBAM-DPN, was proposed. Firstly, data fusion of typical micro-expression datasets was performed. Then, the change values of pixels in the sequence frames were analyzed to determine the position of the apex frame, after that, image enhancement was performed to the apex frame. Finally, based on the CBAM-DPN network, the features of the enhanced micro-expression apex frame was effectively extracted, and a classifier was constructed to recognize the micro-expression. The Unweighted F1-score (UF1) and Unweighted Average Recall (UAR) of the model after optimization can reach 0.720 3 and 0.729 3 respectively, which are improved by 0.048 9 and 0.037 9 respectively compared with those of the DPN model, and are improved by 0.068 3 and 0.078 7 respectively compared with those of the CapsuleNet model. Experimental results show that the CBAM-DPN algorithm combined with the advantages of CBAM and DPN can enhance the information extraction ability of small features, and effectively improve the performance of micro-expression recognition.