具有特征金字塔和地标的人脸检测

准确的面部检测和面部标志定位对于任何面部识别系统都是至关重要的。我们介绍了三个具有不同大小的主干网（MobileNetV2-25，MobileNetV2-100和ResNet101）的单级RCNN，以及一个专门针对WIDER FACE数据集训练的六层特征金字塔。..

Face Detection with Feature Pyramids and Landmarks

Accurate face detection and facial landmark localization are crucial to any face recognition system. We present a series of three single-stage RCNNs with different sized backbones (MobileNetV2-25, MobileNetV2-100, and ResNet101) and a six-layer feature pyramid trained exclusively on the WIDER FACE dataset.We compare the face detection and landmark accuracies using eight context module architectures, four proposed by previous research and four modified versions. We find no evidence that any of the proposed architectures significantly overperform and postulate that the random initialization of the additional layers is at least of equal importance. To show this we present a model that achieves near state-of-the-art performance on WIDER FACE and also provides high accuracy landmarks with a simple context module. We also present results using MobileNetV2 backbones, which achieve over 90% average precision on the WIDER FACE hard validation set while being able to run in real-time. By comparing to other authors, we show that our models exceed the state-of-the-art for similar-sized RCNNs and match the performance of much heavier networks.