基于随机微分方程的多能量引导图像转换用于近红外面部表情识别
Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition
AI总结：
针对近红外面部表情数据稀缺与异构图像转换可控性不足的问题，提出NFER-SDE，利用随机微分方程和领域知识保留模态不变高频信息，在Oulu-CASIA与Large-HFE上达到最先进性能。
摘要：
光照变化一直是真实世界面部表情识别（FER）中的长期挑战。在不受控或非可见光条件下，近红外（NIR）能够提供一种简单且可选的解决方案，以获取高质量图像，并补充可见光域中缺失的几何与纹理细节。由于缺乏现有的大规模NIR面部表情数据集，直接将VIS FER方法扩展到NIR光谱可能效果不佳。此外，以往的异构图像合成方法在没有先验任务知识的情况下，受到可控性较低的限制。为解决这些问题，我们提出了首个称为NIR-FER随机微分方程（NFER-SDE）的方法，该方法在异构模态之间转换面部表情外观，以应对小规模NIR数据上的过拟合问题。NFER-SDE能够将整幅VIS源图像作为输入，并结合特定领域知识，引导保留图像高频内容中的模态不变信息。大量实验和消融研究表明，NFER-SDE显著提升了NIR FER的性能，并在仅有的两个可用NIR FER数据集Oulu-CASIA和Large-HFE上取得了最先进结果。
英文摘要：
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.
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