Abstract
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 microexpressions 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 Casmell 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.