Emotion-specific AUs for micro-expression recognition

Abstract

The Facial Action Coding System (FACS) comprehensively describes facial expressions with facial action units (AUs). It is a well-used technique by researchers in emotions research to understand human emotions better. Most micro-expression datasets provide FACS-coded AU ground truths corresponding to micro-expressions classes. It is commonly accepted in computer vision-based emotions research that certain emotions are reliably revealed when specific combinations of AUs occur. However, the reliability of the ground truth AUs in the micro-expression datasets is lower than that of normal expressions, as they have lower AU intensities. Moreover, these micro-expression datasets only report the overall reliability of all AUs. It could not be identified which AUs had been accurately coded. This work aims to revisit the ground truth AUs of popular micro-expression datasets, namely CASME II, SAMM and CAS(ME)2, and inspect whether any AUs crucial for micro-expression recognition may need to be reconsidered. This paper also provides a detailed AU analysis which yields new AU-based RoIs for each dataset. These new RoIs improve the micro-expression recognition performances compared to the baselines considered in this work. The proposed RoIs for CASME II, SAMM and CAS(ME)2 improve the recognition rates by 2%, 1% and 4%, respectively, when compared with the existing RoIs.