We present BlazeFace, a lightweight and well-performing face detector
tailored for mobile GPU inference. It runs at a speed of 200-1000+ FPS on
flagship devices. This super-realtime performance enables it to be applied to
any augmented reality pipeline that requires an accurate facial region of
interest as an input for task-specific models, such as 2D/3D facial keypoint or
geometry estimation, facial features or expression classification, and face
region segmentation. Our contributions include a lightweight feature extraction
network inspired by, but distinct from MobileNetV1/V2, a GPU-friendly anchor
scheme modified from Single Shot MultiBox Detector (SSD), and an improved tie
resolution strategy alternative to non-maximum suppression.

BlazeFace 是一个轻量且表现良好的人脸检测器，适用于移动 GPU 推理，能够以 200-1000+FPS 的速度运行。它可应用于需要准确面部区域的增强现实流水线，作为 2D / 3D 面部关键点或几何估计，面部特征或表情分类以及面部区域分割的输入。我们的贡献包括一个轻量级的特征提取网络、一个从 SSD 修改的 GPU 友好型锚点方案以及一种改进的连接决策策略。

移动 GPU 上亚毫秒级神经脸部检测

BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs

We introduce the Single Stage Headless (SSH) face detector. Unlike two stage
proposal-classification detectors, SSH detects faces in a single stage directly
from the early convolutional layers in a classification network. SSH is
headless. That is, it is able to achieve state-of-the-art results while
removing the "head" of its underlying classification network -- i.e. all fully
connected layers in the VGG-16 which contains a large number of parameters.
Additionally, instead of relying on an image pyramid to detect faces with
various scales, SSH is scale-invariant by design. We simultaneously detect
faces with different scales in a single forward pass of the network, but from
different layers. These properties make SSH fast and light-weight.
Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based
state-of-the-art on the WIDER dataset. Even though, unlike the current
state-of-the-art, SSH does not use an image pyramid and is 5X faster. Moreover,
if an image pyramid is deployed, our light-weight network achieves
state-of-the-art on all subsets of the WIDER dataset, improving the AP by 2.5%.
SSH also reaches state-of-the-art results on the FDDB and Pascal-Faces datasets
while using a small input size, leading to a runtime of 50 ms/image on a GPU.
The code is available at this https URL

本文提出了一种基于单阶段检测的人脸检测器，采用 Headless VGG-16 进行参数减少，结构轻量化，实现了在 WIDER 数据集上超过 ResNet-101 的最新成果，并在 FDDB 和 Pascal-Faces 数据集上取得了最先进的结果。

SSH: 单阶段无头人脸检测器

SSH: Single Stage Headless Face Detector

In this paper, we share our experience in designing a convolutional
network-based face detector that could handle faces of an extremely wide range
of scales. We show that faces with different scales can be modeled through a
specialized set of deep convolutional networks with different structures. These
detectors can be seamlessly integrated into a single unified network that can
be trained end-to-end. In contrast to existing deep models that are designed
for wide scale range, our network does not require an image pyramid input and
the model is of modest complexity. Our network, dubbed ScaleFace, achieves
promising performance on WIDER FACE and FDDB datasets with practical runtime
speed. Specifically, our method achieves 76.4 average precision on the
challenging WIDER FACE dataset and 96% recall rate on the FDDB dataset with 7
frames per second (fps) for 900 * 1300 input image.

本篇论文介绍了一种基于卷积神经网络设计的人脸检测器，该检测器可以处理极大范围内尺度大小的人脸，所设计的深度卷积神经网络结构可以模拟不同尺度的人脸，且经过训练后在 WIDER FACE 和 FDDB 数据集中表现出优异性能，其模型的输入不需要图像金字塔，拥有适度的复杂度，实用性标准时间内运行。