Facial analysis is a key component in a wide range of applications such as security, autonomous driving, entertainment, and healthcare. Despite the availability of various facial RGB datasets, the thermal modality, which plays a crucial role in life sciences, medicine, and biometrics, has been largely overlooked. To address this gap, we introduce the T-FAKE dataset, a new large-scale synthetic thermal dataset with sparse and dense landmarks. To facilitate the creation of the dataset, we propose a novel RGB2Thermal loss function, which enables the transfer of thermal style to RGB faces. By utilizing the Wasserstein distance between thermal and RGB patches and the statistical analysis of clinical temperature distributions on faces, we ensure that the generated thermal images closely resemble real samples. Using RGB2Thermal style transfer based on our RGB2Thermal loss function, we create the T-FAKE dataset, a large-scale synthetic thermal dataset of faces. Leveraging our novel T-FAKE dataset, probabilistic landmark prediction, and label adaptation networks, we demonstrate significant improvements in landmark detection methods on thermal images across different landmark conventions. Our models show excellent performance with both sparse 70-point landmarks and dense 478-point landmark annotations. Our code and models are available at https://github.com/phflot/tfake.

翻译：面部分析是安全、自动驾驶、娱乐和医疗保健等广泛应用中的关键组成部分。尽管存在各种面部RGB数据集，但在生命科学、医学和生物识别中起关键作用的热成像模态在很大程度上被忽视了。为弥补这一空白，我们引入了T-FAKE数据集，这是一个包含稀疏和稠密关键点的大规模合成热成像数据集。为促进数据集的创建，我们提出了一种新颖的RGB2Thermal损失函数，该函数能够将热成像风格迁移到RGB面部图像上。通过利用热成像与RGB图像块之间的Wasserstein距离，并结合面部临床温度分布的统计分析，我们确保生成的热成像图像与真实样本高度相似。基于我们的RGB2Thermal损失函数进行RGB2Thermal风格迁移，我们创建了T-FAKE数据集——一个大规模的面部合成热成像数据集。借助我们新颖的T-FAKE数据集、概率关键点预测和标签自适应网络，我们证明了在不同关键点标注规范下，热成像图像的关键点检测方法均取得显著改进。我们的模型在稀疏70点关键点和稠密478点关键点标注上均表现出优异性能。我们的代码和模型可在https://github.com/phflot/tfake获取。