  Segmentation of thermal facial images is a challenging task. This is because
facial features often lack salience due to high-dynamic thermal range scenes
and occlusion issues. Limited availability of datasets from unconstrained
settings further limits the use of the state-of-the-art segmentation networks,
loss functions and learning strategies which have been built and validated for
RGB images. To address the challenge, we propose Self-Adversarial Multi-scale
Contrastive Learning (SAM-CL) framework as a new training strategy for thermal
image segmentation. SAM-CL framework consists of a SAM-CL loss function and a
thermal image augmentation (TiAug) module as a domain-specific augmentation
technique. We use the Thermal-Face-Database to demonstrate effectiveness of our
approach. Experiments conducted on the existing segmentation networks (UNET,
Attention-UNET, DeepLabV3 and HRNetv2) evidence the consistent performance
gains from the SAM-CL framework. Furthermore, we present a qualitative analysis
with UBComfort and DeepBreath datasets to discuss how our proposed methods
perform in handling unconstrained situations.
