Changzheng Zhang, Xiang Xu, Dandan Tu
Faster RCNN has achieved great success for generic object detection including PASCAL object detection and MS COCO object detection. In this report, we propose a detailed designed Faster RCNN method named FDNet1.0 for face detection. Several techniques were employed including multi-scale training, multi-scale testing, light-designed RCNN, some tricks for inference and a vote-based ensemble method. Our method achieves two 1th places and one 2nd place in three tasks over WIDER FACE validation dataset (easy set, medium set, hard set).
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Facial Recognition and Modelling | WIDER Face (Medium) | AP | 0.939 | FDNet |
| Facial Recognition and Modelling | WIDER Face (Easy) | AP | 0.95 | FDNet |
| Facial Recognition and Modelling | WIDER Face (Hard) | AP | 0.896 | FDNet |
| Face Detection | WIDER Face (Medium) | AP | 0.939 | FDNet |
| Face Detection | WIDER Face (Easy) | AP | 0.95 | FDNet |
| Face Detection | WIDER Face (Hard) | AP | 0.896 | FDNet |
| Face Reconstruction | WIDER Face (Medium) | AP | 0.939 | FDNet |
| Face Reconstruction | WIDER Face (Easy) | AP | 0.95 | FDNet |
| Face Reconstruction | WIDER Face (Hard) | AP | 0.896 | FDNet |
| 3D | WIDER Face (Medium) | AP | 0.939 | FDNet |
| 3D | WIDER Face (Easy) | AP | 0.95 | FDNet |
| 3D | WIDER Face (Hard) | AP | 0.896 | FDNet |
| 3D Face Modelling | WIDER Face (Medium) | AP | 0.939 | FDNet |
| 3D Face Modelling | WIDER Face (Easy) | AP | 0.95 | FDNet |
| 3D Face Modelling | WIDER Face (Hard) | AP | 0.896 | FDNet |
| 3D Face Reconstruction | WIDER Face (Medium) | AP | 0.939 | FDNet |
| 3D Face Reconstruction | WIDER Face (Easy) | AP | 0.95 | FDNet |
| 3D Face Reconstruction | WIDER Face (Hard) | AP | 0.896 | FDNet |