In face detection, low-resolution faces, such as numerous small faces of a
human group in a crowded scene, are common in dense face prediction tasks. They
usually contain limited visual clues and make small faces less distinguishable
from the other small objects, which poses great challenge to accurate face
detection. Although deep convolutional neural network has significantly
promoted the research on face detection recently, current deep face detectors
rarely take into account low-resolution faces and are still vulnerable to the
real-world scenarios where massive amount of low-resolution faces exist.
Consequently, they usually achieve degraded performance for low-resolution face
detection. In order to alleviate this problem, we develop an efficient detector
termed EfficientSRFace by introducing a feature-level super-resolution
reconstruction network for enhancing the feature representation capability of
the model. This module plays an auxiliary role in the training process, and can
be removed during the inference without increasing the inference time.
Extensive experiments on public benchmarking datasets, such as FDDB and WIDER
Face, show that the embedded image super-resolution module can significantly
improve the detection accuracy at the cost of a small amount of additional
parameters and computational overhead, while helping our model achieve
competitive performance compared with the state-of-the-arts methods.

为解决深度人脸检测在低分辨率人脸检测中的性能问题，本文提出了一种新的高效检测器 EfficientSRFace，引入了特征级超分辨率重建网络，并在公共数据集 FDDB 和 WIDER Face 上取得了与现有算法相当的检测性能结果。

EfficientSRFace: 一种高效的具有超分辨增强的网络，用于准确的面部检测

EfficientSRFace: An Efficient Network with Super-Resolution Enhancement  for Accurate Face Detection

Convolutional Neural Networks have reached extremely high performances on the
Face Recognition task. Largely used datasets, such as VGGFace2, focus on
gender, pose and age variations trying to balance them to achieve better
results. However, the fact that images have different resolutions is not
usually discussed and resize to 256 pixels before cropping is used. While
specific datasets for very low resolution faces have been proposed, less
attention has been payed on the task of cross-resolution matching. Such
scenarios are of particular interest for forensic and surveillance systems in
which it usually happens that a low-resolution probe has to be matched with
higher-resolution galleries. While it is always possible to either increase the
resolution of the probe image or to reduce the size of the gallery images, to
the best of our knowledge an extensive experimentation of cross-resolution
matching was missing in the recent deep learning based literature. In the
context of low- and cross-resolution Face Recognition, the contributions of our
work are: i) we proposed a training method to fine-tune a state-of-the-art
model in order to make it able to extract resolution-robust deep features; ii)
we tested our models on the benchmark datasets IJB-B/C considering images at
both full and low resolutions in order to show the effectiveness of the
proposed training algorithm. To the best of our knowledge, this is the first
work testing extensively the performance of a FR model in a cross-resolution
scenario; iii) we tested our models on the low resolution and low quality
datasets QMUL-SurvFace and TinyFace and showed their superior performances,
even though we did not train our model on low-resolution faces only and our
main focus was cross-resolution; iv) we showed that our approach can be more
effective with respect to preprocessing faces with super resolution techniques.

本文提出了一种训练方法来 fine-tune 现代的人脸识别模型，以帮助它们提取抗分辨率的深层特征，同时也考虑了高低分辨率和低质量人脸识别的问题，并展示了使用超分辨率技术进行预处理可以更有效地使用我们的方法。