利用热传感器辅助标签生成用于生理评估的热成像人脸
Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels
AI总结：
本文提出 favtGAN，利用 pix2pix 和辅助传感器标签预测网络实现可见光到热成像人脸生成，并通过合并人脸与城市景观数据集提升 SSIM 和 PSNR。
摘要：
热图像揭示了关于人体压力、炎症迹象和情绪状态等在可见光图像上无法看到的具有重要医学意义的生理信息。提供一种从可见光图像生成热成像人脸的方法，对于远程医疗领域展示这些医学信息将具有极高的价值。据我们所知，目前可见光到热成像（VT）人脸转换的研究工作十分有限，许多现有工作走的是相反方向，即为执法应用从热监控图像生成可见光人脸（TV）。因此，我们提出了 favtGAN，这是一种 VT GAN，它使用 pix2pix 图像转换模型，并带有一个辅助传感器标签预测网络，用于从可见光图像生成热成像人脸。由于大多数 TV 方法仅使用来自单一热传感器的一个数据源进行训练，我们合并了来自人脸和城市景观的数据集。这些合并数据采集自相似的传感器，以引导训练和迁移学习任务，这一点尤其有价值，因为可见光-热成像人脸数据集十分有限。在这些合并数据集上的实验表明，与仅在单一人脸数据集上训练相比，favtGAN 在生成热成像人脸的 SSIM 和 PSNR 分数上均有提升。
英文摘要：
Thermal images reveal medically important physiological information about human stress, signs of inflammation, and emotional mood that cannot be seen on visible images. Providing a method to generate thermal faces from visible images would be highly valuable for the telemedicine community in order to show this medical information. To the best of our knowledge, there are limited works on visible-to-thermal (VT) face translation, and many current works go the opposite direction to generate visible faces from thermal surveillance images (TV) for law enforcement applications. As a result, we introduce favtGAN, a VT GAN which uses the pix2pix image translation model with an auxiliary sensor label prediction network for generating thermal faces from visible images. Since most TV methods are trained on only one data source drawn from one thermal sensor, we combine datasets from faces and cityscapes. These combined data are captured from similar sensors in order to bootstrap the training and transfer learning task, especially valuable because visible-thermal face datasets are limited. Experiments on these combined datasets show that favtGAN demonstrates an increase in SSIM and PSNR scores of generated thermal faces, compared to training on a single face dataset alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
