Objective Classes for Micro-Facial Expression Recognition
This addresses the issue of human reporting bias in micro-expression recognition for applications like lie detection or affective computing, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of bias in micro-expression recognition by proposing to classify expressions using Action Units instead of predicted emotions, achieving 86.35% accuracy on the CASME II dataset with HOG 3D, which outperforms the state-of-the-art emotional-based classification.
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.