Objective Classes for Micro-Facial Expression Recognition

TL;DR

This paper proposes using Action Units for classifying micro-expressions to reduce bias and improve recognition accuracy, demonstrating superior results on benchmark datasets compared to emotion-based classes.

Contribution

It introduces an objective classification scheme based on Action Units for micro-expressions, outperforming emotion-based methods on CASME II and SAMM datasets.

Findings

Achieved 86.35% accuracy on CASME II with HOG 3D.

Outperformed state-of-the-art emotion-based classification methods.

Validated that Action Unit-based classification reduces bias and improves recognition.

Abstract

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.