Abstract
A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.
Highlights
Emotions are the inner feelings of human beings and expressions are the windows of human emotions
The recognition performance achieved by MicroExpSTCNN (66.07%), Residual Network (RESNET)-3D (65.01%), and Basic Network (67.78%) do not distinct from each other significantly, which could be that they both used neural networks
The recognition performance achieved by MicroExpSTCNN (50.92%), RESNET-3D
Summary
Emotions are the inner feelings of human beings and expressions are the windows of human emotions. A micro-expression on the face is a unique expression that happens spontaneously. When humans try to conceal their true emotions, the restrained feelings are shown by fast muscular movement out of a spontaneous physical reaction [1]. A micro-expression is one of the foundations for the judgment of human psychological status. In contrast to normal facial expressions that usually sustained 1/2 s to 4 s [2], the micro-expressions of humans last so short that they tend to be neglected. The duration of a micro-expression is only
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