  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.
