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Found 6 papers, 1 papers with code
A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset
Thermal face imagery, which captures the naturally emitted heat from the face, is limited in availability compared to face imagery in the visible spectrum.
Cross-Domain Identification for Thermal-to-Visible Face Recognition
Recent advances in domain adaptation, especially those applied to heterogeneous facial recognition, typically rely upon restrictive Euclidean loss functions (e. g., $L_2$ norm) which perform best when images from two different domains (e. g., visible and thermal) are co-registered and temporally synchronized.
Multi-Scale Thermal to Visible Face Verification via Attribute Guided Synthesis
Finally, a pre-trained VGG-Face network is leveraged to extract features from the synthesized image and the input visible image for verification.
Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis
Polarimetric thermal to visible face verification entails matching two images that contain significant domain differences.
Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks
Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible image given the corresponding polarimetric signatures.
Thermal to Visible Synthesis of Face Images using Multiple Regions
Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively.
