Deep learning enhancement of infrared face images using generative adversarial networks

This work presents a deep learning framework based on the use of deep convolutional generative adversarial networks (DCGAN) for infrared face image super-resolution. We use DCGAN for upscaling the images by a factor of 4 x 4, starting at a size of 16 x 16 and obtaining a 64 x 64 face image. Tests are conducted using different infrared face datasets operating in the near-infrared (NIR) and the long-wave infrared (LWIR) spectrum. We can see that the proposed framework performs well and preserves important details of the face. This kind of approach can be very useful in security applications where we can scan faces in the crowd or detect faces at a distance and upscale them for further recognition through an infrared or a multispectral face recognition system. (C) 2018 Optical Society of America