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Generating visible-like face images from thermal images is essential toperform manual and automatic cross-spectrum face recognition. We successfullypropose a solution based on cascaded refinement network that, unlike previousworks, produces high quality generated color images without the need for facealignment, large databases, data augmentation, polarimetric sensors,computationally-intense training, or unrealistic restriction on the generatedresolution. The training of our solution is based on the contextual loss,making it inherently scale (face area) and rotation invariant. We presentgenerated image samples of unknown individuals under different poses andocclusion conditions.We also prove the high similarity in image quality betweenground-truth images and generated ones by comparing seven quality metrics. Wecompare our results with two state-of-the-art approaches proving thesuperiority of our proposed approach.

Cascaded Generation of High-quality Color Visible Face Images from Thermal Captures