Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs

TL;DR

Super-FAN is an end-to-end GAN-based system that simultaneously enhances low-resolution face images and accurately locates facial landmarks across various poses and real-world conditions.

Contribution

It introduces the first integrated system for joint face super-resolution and landmark localization, incorporating structural information via heatmap regression.

Findings

Significant improvement over state-of-the-art in face super-resolution.

Enhanced facial landmark detection accuracy across diverse poses.

Effective on real-world low-resolution images, not just synthetic data.

Abstract

This paper addresses 2 challenging tasks: improving the quality of low resolution facial images 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 illustrate the benefit of training the two networks jointly by reporting good results not only on frontal images (as in prior work) but on the whole spectrum of facial poses, and not only on synthetic low resolution images (as in prior work) but also on real-world images. (c) We 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 images.