Detecting Faces Using Region-based Fully Convolutional Networks
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.
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Tasks
Datasets
WIDER FACE
FDDB
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Face Detection | FDDB | Face R-FCN | AP | 0.990 | # 2 | |
| Face Detection | WIDER Face (Easy) | Face R-FCN | AP | 0.943 | # 20 | |
| Face Detection | WIDER Face (Hard) | Face R-FCN | AP | 0.876 | # 12 | |
| Face Detection | WIDER Face (Medium) | Face R-FCN | AP | 0.931 | # 20 |
