Abstract
Face detection techniques have been developed for
decades, and one of remaining open challenges is detecting small faces in unconstrained conditions. The reason is
that tiny faces are often lacking detailed information and
blurring. In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small
one by adopting a generative adversarial network (GAN).
Toward this end, the basic GAN formulation achieves it by
super-resolving and refining sequentially (e.g. SR-GAN and
cycle-GAN). However, we design a novel network to address
the problem of super-resolving and refining jointly. We also
introduce new training losses to guide the generator network to recover fine details and to promote the discriminator network to distinguish real vs. fake and face vs. non-face