ThermVision-DB presents a synthetic long-wave infrared (LWIR) facial dataset designed to support research in privacy-preserving vision, thermal perception, and multimodal facial analysis. The dataset builds upon generative diffusion models to create photorealistic thermal facial images and video sequences capturing controlled variations in facial expression and head pose. Each synthetic identity is generated using text-to-image conditioning followed by video retargeting module, enabling precise control over pose angles, expression intensity, and frame-to-frame consistency. The dataset includes a diverse set of synthetic adult identities of both male and female genders with multiple facial expressions - such as neutral, smile, frown, and surprise and head-pose rotations spanning yaw, pitch, and roll. Data are provided in both image and video formats, accompanied by face localization annotations, landmark detections and identity labels. To ensure reusability and scalability, all samples are generated through a standardized pipeline using open-source models, allowing researchers to easily expand the dataset with additional synthetic identities while maintaining consistent thermal appearance and scene illumination. The synthetic generation process avoids the use of any personally identifiable visual data, ensuring compliance with FAIR and GDPR principles. ThermVision-DB is intended for use in developing and benchmarking algorithms for facial detection, landmark localization, expression recognition, and head-pose estimation in the thermal domain. It also provides a foundation for research in synthetic-to-real transfer learning, privacy-safe biometric analysis, and cross-spectrum data fusion. The dataset is released for open research purposes under a non-commercial license, with full documentation and metadata available to facilitate reproducibility and integration with existing thermal vision benchmarks.
Farooq et al. (Wed,) studied this question.