Papers › Detecting Faces Using Region-based Fully Convolutional Networks
Detecting Faces Using Region-based Fully Convolutional Networks
Yitong Wang, Xing Ji, Zheng Zhou, Hao Wang, Zhifeng Li
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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Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Detection | FDDB | Face R-FCN | AP | 0.990 | #2 of 11 | Archive leaderboard | report |
| Face Detection | WIDER Face (Easy) | Face R-FCN | AP | 0.943 | #19 of 27 | Archive leaderboard | report |
| Face Detection | WIDER Face (Hard) | Face R-FCN | AP | 0.876 | #11 of 40 | Archive leaderboard | report |
| Face Detection | WIDER Face (Medium) | Face R-FCN | AP | 0.931 | #19 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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