  Facial expression recognition (FER) methods have made great inroads in
categorising moods and feelings in humans. Beyond FER, pain estimation methods
assess levels of intensity in pain expressions, however assessing the quality
of all facial expressions is of critical value in health-related applications.
In this work, we address the quality of five different facial expressions in
patients affected by Parkinson's disease. We propose a novel landmark-guided
approach, QAFE-Net, that combines temporal landmark heatmaps with RGB data to
capture small facial muscle movements that are encoded and mapped to severity
scores. The proposed approach is evaluated on a new Parkinson's Disease Facial
Expression dataset (PFED5), as well as on the pain estimation benchmark, the
UNBC-McMaster Shoulder Pain Expression Archive Database. Our comparative
experiments demonstrate that the proposed method outperforms SOTA action
quality assessment works on PFED5 and achieves lower mean absolute error than
the SOTA pain estimation methods on UNBC-McMaster. Our code and the new PFED5
dataset are available at https://github.com/shuchaoduan/QAFE-Net.
