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.

本文提出一种联合对齐和超分辨率网络，通过共享深度编码器提取特征，实现同时检测面部标记和超分辨率。实验证明，该模型在检测微小面部标记和超分辨率方面优于现有方法。

微小人脸的联合超分辨率与对齐

Joint Super-Resolution and Alignment of Tiny Faces

This paper introduces a novel anchor design to support anchor-based face
detection for superior scale-invariant performance, especially on tiny faces.
To achieve this, we explicitly address the problem that anchor-based detectors
drop performance drastically on faces with tiny sizes, e.g. less than 16x16
pixels. In this paper, we investigate why this is the case. We discover that
current anchor design cannot guarantee high overlaps between tiny faces and
anchor boxes, which increases the difficulty of training. The new Expected Max
Overlapping (EMO) score is proposed which can theoretically explain the low
overlapping issue and inspire several effective strategies of new anchor design
leading to higher face overlaps, including anchor stride reduction with new
network architectures, extra shifted anchors, and stochastic face shifting.
Comprehensive experiments show that our proposed method significantly
outperforms the baseline anchor-based detector, while consistently achieving
state-of-the-art results on challenging face detection datasets with
competitive runtime speed.

本文介绍一种新的 anchor 设计，用于支持基于 anchor 的人脸检测，并提升其在微小脸部的尺度不变性表现。新的 “期望最大重叠”（EMO）得分被提出，可以理论上解释低重叠问题，并启发几种新的 anchor 设计策略。 经过全面的实验测试，新的方法显著优于基线 anchor 检测器，同时以竞争性的运行时间速度持续取得挑战性人脸检测数据集的最先进成果。