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Thermal Face Generation Using StyleGAN
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DOI：10.1109/ACCESS.2021.3085423.png)
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Abstract
En 中文
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.
Keywords:
Face recognition
Generative adversarial networks
Deep learning
Generators
Databases
Data models
Training
Generative adversarial networks
StyleGAN2
thermal face recognition
deep learning
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