Proof-of-Concept Techniques for Generating Synthetic Thermal Facial Data for Training of Deep Learning Models

Abstract

Thermal imaging has played a dynamic role in the diversified field of consumer technology applications. To build artificially intelligent thermal imaging systems, large scale thermal datasets are required for successful convergence of complex deep learning models. In this study, we have highlighted various techniques for generating large scale synthetic facial thermal data using both public and locally gathered datasets. It includes data augmentation, synthetic data generation using StyleGAN network, and 2D to 3D image reconstruction using deep learning architectures. Training and validation accuracy of Wide ResNet CNN for binary gender recognition task is improved by 4.6% and 4.4% using original and newly generated synthetic data with an overall test accuracy of 83.33%.