Facial analysis is a key component in a wide range of ap-plications such as security, autonomous driving, entertainment, and healthcare. Despite the availability of various fa-cial RGB datasets, the thermal modality, which plays a crucial role in life sciences, medicine, and biometrics, has beenlargely overlooked. To address this gap, we introduce the T-FAKE dataset, a new large-scale synthetic thermal datasetwith sparse and dense landmarks. To facilitate the creationof the dataset, we propose a novel RGB2Thermal loss function, which enables the domain-adaptive transfer of thermal style to RGB faces. By utilizing the Wasserstein distance between thermal and RGB patches and the statisticalanalysis of clinical temperature distributions on faces, weensure that the generated thermal images closely resemblereal samples. Using RGB2Thermal style transfer based onour RGB2Thermal loss function, we create the large-scalesynthetic thermal T-FAKE dataset. Leveraging our novel T-FAKE dataset, probabilistic landmark prediction, and labeladaptation networks, we demonstrate significant improvements in landmark detection methods on thermal imagesacross different landmark conventions. Our models showexcellent performance with both sparse 70-point landmarksand dense 478-point landmark annotations