  Super-resolution (SR) and landmark localization of tiny faces are highly
correlated tasks. On the one hand, landmark localization could obtain higher
accuracy with faces of high-resolution (HR). On the other hand, face SR would
benefit from prior knowledge of facial attributes such as landmarks. Thus, we
propose a joint alignment and SR network to simultaneously detect facial
landmarks and super-resolve tiny faces. More specifically, a shared deep
encoder is applied to extract features for both tasks by leveraging
complementary information. To exploit the representative power of the
hierarchical encoder, intermediate layers of a shared feature extraction module
are fused to form efficient feature representations. The fused features are
then fed to task-specific modules to detect landmarks and super-resolve face
images in parallel. Extensive experiments demonstrate that the proposed model
significantly outperforms the state-of-the-art in both landmark localization
and SR of faces. We show a large improvement for landmark localization of tiny
faces (i.e., 16*16). Furthermore, the proposed framework yields comparable
results for landmark localization on low-resolution (LR) faces (i.e., 64*64) to
existing methods on HR (i.e., 256*256). As for SR, the proposed method recovers
sharper edges and more details from LR face images than other state-of-the-art
methods, which we demonstrate qualitatively and quantitatively.
