  We propose a novel single face image super-resolution method, which named
Face Conditional Generative Adversarial Network(FCGAN), based on boundary
equilibrium generative adversarial networks. Without taking any facial prior
information, our method can generate a high-resolution face image from a
low-resolution one. Compared with existing studies, both our training and
testing phases are end-to-end pipeline with little pre/post-processing. To
enhance the convergence speed and strengthen feature propagation, skip-layer
connection is further employed in the generative and discriminative networks.
Extensive experiments demonstrate that our model achieves competitive
performance compared with state-of-the-art models.
