Face detection has achieved great success using the region-based methods. In this report, we propose a region-based face detector applying deep networks in a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully Convolutional Networks (R-FCN), our face detector is more accurate and computational efficient compared with the previous R-CNN based face detectors. In our approach, we adopt the fully convolutional Residual Network (ResNet) as the backbone network. Particularly, We exploit several new techniques including position-sensitive average pooling, multi-scale training and testing and on-line hard example mining strategy to improve the detection accuracy. Over two most popular and challenging face detection benchmarks, FDDB and WIDER FACE, Face R-FCN achieves superior performance over state-of-the-arts.
@article{arxiv.1709.05256,
title = {Detecting Faces Using Region-based Fully Convolutional Networks},
author = {Yitong Wang and Xing Ji and Zheng Zhou and Hao Wang and Zhifeng Li},
journal= {arXiv preprint arXiv:1709.05256},
year = {2017}
}