  Illumination variation has been a long-term challenge in real-world facial
expression recognition(FER). Under uncontrolled or non-visible light
conditions, Near-infrared (NIR) can provide a simple and alternative solution
to obtain high-quality images and supplement the geometric and texture details
that are missing in the visible domain. Due to the lack of existing large-scale
NIR facial expression datasets, directly extending VIS FER methods to the NIR
spectrum may be ineffective. Additionally, previous heterogeneous image
synthesis methods are restricted by low controllability without prior task
knowledge. To tackle these issues, we present the first approach, called for
NIR-FER Stochastic Differential Equations (NFER-SDE), that transforms face
expression appearance between heterogeneous modalities to the overfitting
problem on small-scale NIR data. NFER-SDE is able to take the whole VIS source
image as input and, together with domain-specific knowledge, guide the
preservation of modality-invariant information in the high-frequency content of
the image. Extensive experiments and ablation studies show that NFER-SDE
significantly improves the performance of NIR FER and achieves state-of-the-art
results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.
