Face Hallucination Using Generative Adversarial Networks

Abstract

The evolution of surveillance techniques in the modern era has powered AI to develop algorithms that provide numerous opportunities to improve security and well being. Surveillance cameras monitoring a wide field of view can result in capturing indiscernible faces which makes human activity monitoring and recognition impossible. Single Image Super-resolution (SISR) provides a feasible solution to enhance such faces in order to recognise the individuals. However, lack of ground truth high-resolution methods make it infeasible to develop such devised methods to develop a standard SISR model. In this research, I explore the challenge of building a SISR system from a limited set of unaligned pairs of Low-res and High-res images. Though none of the models could produce substantial results, each one of those unravel problems in an unsupervised setting.

I propose a model that leverages literature from Style Transfer to achieve the reconstruction of noisy low-res face images, but in an extremely specific domain. I further conclude the research for exploration of models and domains in Deep Learning where we might find the possible solution to the problem.