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.

通过 NIR-FER 随机微分方程方法，克服了面部表情在照明变化和可见光条件下的识别难题，解决了可见光附加几何和纹理详细信息的不足问题，并在两个现有的 NIR FER 数据集 Oulu-CASIA 和 Large-HFE 上取得了最先进的结果。

多能量指导的带随机微分方程的近红外面部表情识别图像翻译

Multi-Energy Guided Image Translation with Stochastic Differential  Equations for Near-Infrared Facial Expression Recognition

Drastic variations in illumination across surveillance cameras make the
person re-identification problem extremely challenging. Current large scale
re-identification datasets have a significant number of training subjects, but
lack diversity in lighting conditions. As a result, a trained model requires
fine-tuning to become effective under an unseen illumination condition. To
alleviate this problem, we introduce a new synthetic dataset that contains
hundreds of illumination conditions. Specifically, we use 100 virtual humans
illuminated with multiple HDR environment maps which accurately model realistic
indoor and outdoor lighting. To achieve better accuracy in unseen illumination
conditions we propose a novel domain adaptation technique that takes advantage
of our synthetic data and performs fine-tuning in a completely unsupervised
way. Our approach yields significantly higher accuracy than semi-supervised and
unsupervised state-of-the-art methods, and is very competitive with supervised
techniques.

本研究通过构建包含数百种照明条件的新的合成数据集以及采用一种新颖的领域自适应技术来解决监控摄像头之间照明差异引起的重识别问题，取得了与监督技术相当的效果。