Detecting Faces Using Region-based Fully Convolutional Networks

Abstract

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.

References in corpus (1)

• Face Detection with the Faster R-CNN

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• SFA: Small Faces Attention Face Detector
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• Segmentation is All You Need
• ASFD: Automatic and Scalable Face Detector
• Unsupervised Hard Example Mining from Videos for Improved Object Detection
• DSFD: Dual Shot Face Detector
• Robust Face Detection via Learning Small Faces on Hard Images
• Survey of Face Detection on Low-quality Images
• KPNet: Towards Minimal Face Detector
• FoxNet: A Multi-face Alignment Method
• Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection
• FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection
• KL-Divergence-Based Region Proposal Network for Object Detection
• MSFD:Multi-Scale Receptive Field Face Detector