  Recent advances in facial landmark detection achieve success by learning
discriminative features from rich deformation of face shapes and poses. Besides
the variance of faces themselves, the intrinsic variance of image styles, e.g.,
grayscale vs. color images, light vs. dark, intense vs. dull, and so on, has
constantly been overlooked. This issue becomes inevitable as increasing web
images are collected from various sources for training neural networks. In this
work, we propose a style-aggregated approach to deal with the large intrinsic
variance of image styles for facial landmark detection. Our method transforms
original face images to style-aggregated images by a generative adversarial
module. The proposed scheme uses the style-aggregated image to maintain face
images that are more robust to environmental changes. Then the original face
images accompanying with style-aggregated ones play a duet to train a landmark
detector which is complementary to each other. In this way, for each face, our
method takes two images as input, i.e., one in its original style and the other
in the aggregated style. In experiments, we observe that the large variance of
image styles would degenerate the performance of facial landmark detectors.
Moreover, we show the robustness of our method to the large variance of image
styles by comparing to a variant of our approach, in which the generative
adversarial module is removed, and no style-aggregated images are used. Our
approach is demonstrated to perform well when compared with state-of-the-art
algorithms on benchmark datasets AFLW and 300-W. Code is publicly available on
GitHub: https://github.com/D-X-Y/SAN
