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For a variety of biometric cross-spectral tasks, Visible-Thermal (VT) facialpairs are used. However, due to a lack of calibration in the lab, photographiccapture between two different sensors leads to severely misaligned pairs thatcan lead to poor results for person re-identification and generative AI. Tosolve this problem, we introduce our approach for VT image registration calledVista Morph. Unlike existing VT facial registration that requires manual,hand-crafted features for pixel matching and/or a supervised thermal reference,Vista Morph is completely unsupervised without the need for a reference. Bylearning the affine matrix through a Vision Transformer (ViT)-based SpatialTransformer Network (STN) and Generative Adversarial Networks (GAN), VistaMorph successfully aligns facial and non-facial VT images. Our approach learnswarps in Hard, No, and Low-light visual settings and is robust to geometricperturbations and erasure at test time. We conduct a downstream generative AItask to show that registering training data with Vista Morph improves subjectidentity of generated thermal faces when performing V2T image translation.

Vista-Morph: Unsupervised Image Registration of Visible-Thermal Facial Pairs