Micro and Macro Facial Expression Recognition Using Advanced Local Motion Patterns

In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative Local Motion Patterns (LMP) feature. The LMP feature analyzes locally the motion distribution in order to separate consistent mouvement patterns from noise. Indeed, facial motion extracted from the face is generally noisy and without specific processing, it can hardly cope with expression recognition requirements especially for micro-expressions. Direction and magnitude statistical profiles are jointly analyzed in order to filter out noise. This work presents three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition leading the way to supporting a wide range of expression intensities. The third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variations and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-ranked state-of-the-art methods for macro expression recognition.

Automated summary

In this paper, a new method for facial expression recognition is developed based on Local Motion Patterns (LMP) feature, which separates consistent motion patterns from noise. The method analyzes the elasticity and deformations of facial skin during expressions, and provides a unified approach for recognizing both macro and micro expressions. It also tackles challenges in in-the-wild expression recognition such as lighting variations and head pose changes.