Thermal Face Generation Using StyleGAN

Gabriel Hermosilla, Diego Ignacio Henriquez Tapia, Hector Allende-Cid, Gonzalo Farias Castro, Esteban Vera

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


This article proposes the use of generative adversarial networks (GANs) via StyleGAN2 to create high-quality synthetic thermal images and obtain training data to build thermal face recognition models using deep learning. We employed different variants of StyleGAN2, incorporating the new improved version of StyleGAN that uses adaptive discriminator augmentation (ADA). In addition, three different thermal databases from the literature were employed to train a thermal face detector based on YOLOv3 and to train StyleGAN2 and its variants, evaluating different metrics. The synthetic thermal database was built using GANSpace to manipulate the intermediate latent space w of StyleGAN2 and obtain images with different characteristics, such as eyeglasses, rotation, beards, etc. We carried out the training of 6 pretrained deep learning models for face recognition to validate the use of our synthetic thermal database, obtaining 99.98% accuracy for classifying synthetic thermal face images.

Original languageEnglish
Article number9445031
Pages (from-to)80511-80523
Number of pages13
JournalIEEE Access
StatePublished - 2021


  • Generative adversarial networks
  • StyleGAN2
  • deep learning
  • thermal face recognition


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