Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a human’s real intent. There has been some interest in micro-expression analysis, however, a great majority of the methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. A novel methodology for micro-expression recognition using the Riesz pyramid, a multi-scale steerable Hilbert transform is presented. In fact, an image sequence is transformed with this tool, then the image phase variations are extracted and filtered as proxies for motion. Furthermore, the dominant orientation constancy from the Riesz transform is exploited to average the micro-expression sequence into an image pair. Based on that, the Mean Oriented Riesz Feature description is introduced. Finally the performance of our methods are tested in two spontaneous micro-expressions databases and compared to state-of-the-art methods.

微表情是简短而微妙的面部表情，可在不到一秒的时间内出现在面部。这种面部表情通常发生在高风险的情况下，被认为反映了人类的真实意图。微表达分析引起了人们的兴趣，但是，大多数方法都基于经典建立的计算机视觉方法，例如局部二进制模式，梯度直方图和光流。提出了一种使用Riesz金字塔进行微表情识别的新颖方法，即多尺度可控Hilbert变换。实际上，使用此工具可以转换图像序列，然后提取图像相位变化并过滤为运动的代理。此外，利用Riesz变换的主导方向恒定性将微表达序列平均为图像对。在此基础上，介绍了均值Riesz特征描述。最后，我们的方法的性能在两个自发的微表达数据库中进行了测试，并与最新方法进行了比较。