  Facial expression analysis has long been an active research area of computer
vision. Traditional methods mainly analyse images for prototypical discrete
emotions; as a result, they do not provide an accurate depiction of the complex
emotional states in humans. Furthermore, illumination variance remains a
challenge for face analysis in the visible light spectrum. To address these
issues, we propose using a dimensional model based on valence and arousal to
represent a wider range of emotions, in combination with near infra-red (NIR)
imagery, which is more robust to illumination changes. Since there are no
existing NIR facial expression datasets with valence-arousal labels available,
we present two complementary data augmentation methods (face morphing and
CycleGAN approach) to create NIR image datasets with dimensional emotion labels
from existing categorical and/or visible-light datasets. Our experiments show
that these generated NIR datasets are comparable to existing datasets in terms
of data quality and baseline prediction performance.
