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Found 3 papers, 1 papers with code
Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition
Our proposed framework is composed of modified networks for extracting the most correlated intermediate representations from off-pose thermal and frontal visible face imagery, a sub-network to jointly bridge domain and pose gaps, and a joint-loss function comprised of cross-spectrum and pose-correction losses.
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.
