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。