Micro-expressions are very brief involuntary facial expressions which appear on the face of humans when they unconsciously conceal an emotion. Creating a solution allowing an automatic recognition of the facial micro-expressions from video sequences has garnered increasing attention from experts across such different disciplines as computer science, security, and psychology. This paper offered a solution to facial micro-expressions recognition, based on accordion spatio-temporal representation and Random Forests. The proposed feature space, called “Uniform Local Binary Patterns on an Accordion 2D representation of sub-regions presented by a Pyramid of levels (LBPAccP^u2)”, exploits the effectiveness of uniform LBP patterns applied on an accordion representation of sub-regions at different sizes. Random Forests were used to select the most discriminating features and reduce the classification ambiguity of similar micro-expressions through a new proximity measure. The main objective of our paper was to demonstrate that the use of few features could be more efficient to produce a strong micro-expression recognition classifier that outperforms the approaches that rely on high dimensional features space. The experimental results across six micro-expression datasets show the effectiveness of the proposed solution with an accuracy rate that can reach 81.38% on CasmeII dataset. Compared to some famous competitive state-of-the-art approaches, the proposed solution proved its performance thanks to its accuracy rate as well as the number of features it uses.

微表情是一种非常简短的不自觉的面部表情，当人们无意识地隐藏某种情绪时会出现在他们的脸上。创建一个允许从视频序列中自动识别面部微表情的解决方案引起了计算机科学、安全和心理学等不同学科专家的越来越多的关注。本文提出了一种基于手风琴时空表示和随机森林的面部微表情识别解决方案。提议的特征空间，称为“在由级别金字塔 (LBPAccP ^{u 2)}呈现的子区域的手风琴 2D 表示上的统一局部二进制模式)”，利用统一 LBP 模式的有效性，应用于不同大小的子区域的手风琴表示。随机森林被用来选择最具辨别力的特征，并通过一种新的邻近度量来减少相似微表情的分类模糊性。我们论文的主要目的是证明使用少量特征可以更有效地产生一个强大的微表情识别分类器，该分类器优于依赖高维特征空间的方法。六个微表情数据集的实验结果表明所提出的解决方案的有效性，在 CasmeII 数据集上的准确率可以达到 81.38%。与一些著名的竞争最先进的方法相比，