Thermal Face Generation Using StyleGAN

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.

Automated summary

This article utilizes StyleGAN2 and generative adversarial networks (GANs) to create high-quality synthetic thermal images and build thermal face recognition models using deep learning. By training with thermal databases and pretrained deep learning models, the synthetic thermal database achieved 99.98% accuracy in classifying thermal face images.