Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs
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This paper addresses two challenging tasks: improving the quality of real-world low resolution face images via super-resolution and accurately locating the facial landmarks on such poor resolution images. To this end, we make the following 5 contributions: (a) we propose Super-FAN: the very first end-to-end system that addresses both tasks simultaneously, i.e. both improves face resolution and detects the facial landmarks. The novelty or Super-FAN lies in incorporating structural information in a GAN-based super-resolution algorithm via integrating a sub-network for face alignment through heatmap regression and optimizing a novel heatmap loss. (b) We go beyond the state-of-the-art and illustrate the benefit of training the two networks jointly by reporting results not only on frontal images (as in prior work) but on the whole spectrum of facial poses, and not only on synthetically generated low resolution images (as in prior work) but also on real-world images. (c) We also improve upon the state-of-the-art in face super-resolution by proposing a new residual-based architecture. (d) Quantitatively, we show large improvement over the state-of-the-art for both face super-resolution and alignment. (e) Qualitatively, we show for the first time good results on real-world low resolution facial images.
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