Paper Info
Title
Objective Classes for Micro-Facial Expression 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 (Chinese Academy of Sciences Micro-expression II) 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 (Local Binary Patterns from Three Orthogonal Planes), HOOF (Histograms of Oriented Optical Flow) and HOG 3D (3D Histogram of Oriented Gradient) feature descriptors. The experiments are evaluated on two benchmark FACS (Facial Action Coding System) coded datasets: CASME II and SAMM (A Spontaneous Micro-Facial Movement). 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.
Year
DOI
Venue
2018
10.3390/jimaging4100119
JOURNAL OF IMAGING
Keywords
DocType
Volume
micro-facial expression,expression recognition,action unit
Journal
4
Issue
Citations 
PageRank 
10
7
0.48
References 
Authors
26
3
Name
Order
Citations
PageRank
Adrian K. Davison1996.51
Walied Merghani291.18
Moi Hoon Yap319027.82