Facial micro-expression recognition based on accordion spatio-temporal representation and random forests

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.