Advanced local motion patterns for macro and micro facial expression recognition

Abstract

In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative local motion patterns feature, with 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. And, 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 variation and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-rank state-of-the-art methods for macro expression recognition.

References in corpus (6)

• Going Deeper in Facial Expression Recognition using Deep Neural Networks
• Automatic Facial Expression Recognition Using Features of Salient Facial Patches
• Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines
• A Deep Learning Perspective on the Origin of Facial Expressions
• Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition
• Spontaneous Facial Micro-Expression Recognition using Discriminative Spatiotemporal Local Binary Pattern with an Improved Integral Projection

Cited by in corpus (3)

• Artificial Intelligence can Recognize Whether a Job Applicant is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers
• Facial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation
• Impact of facial landmark localization on facial expression recognition