Facial landmark tracking for thermal images requires tracking certain
important regions of subjects' faces, using images from thermal images, which
omit lighting and shading, but show the temperatures of their subjects. The
fluctuations of heat in particular places reflect physiological changes like
bloodflow and perspiration, which can be used to remotely gauge things like
anxiety and excitement. Past work in this domain has been limited to only a
very limited set of architectures and techniques. This work goes further by
trying a comprehensive suit of various models with different components, such
as residual connections, channel and feature-wise attention, as well as the
practice of ensembling components of the network to work in parallel. The best
model integrated convolutional and residual layers followed by a channel-wise
self-attention layer, requiring less than 100K parameters.

通过使用来自红外图像的热图像，利用人脸的重要区域进行面部标志跟踪，忽略了光照和阴影，但显示了主体的温度，通过特定位置上的热量波动反映生理变化，如血液流动和出汗，用于远程判断焦虑和兴奋。本研究进一步尝试了各种模型，包括残差连接、通道和特征关注以及网络组件的并行运作，最佳模型整合了卷积和残差层以及通道自注意层，只需要不到 100,000 个参数。