Robust and High Performance Face Detector
arXiv:1901.02350
Abstract
In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-of-the-art detector SRN and apply some tricks proposed in the recent literatures to obtain an extremely strong face detector, named VIM-FD. In specific, we exploit more powerful backbone network like DenseNet-121, revisit the data augmentation based on data-anchor-sampling proposed in PyramidBox, and use the max-in-out label and anchor matching strategy in SFD. In addition, we also introduce the attention mechanism to provide additional supervision. Over the most popular and challenging face detection benchmark, i.e., WIDER FACE, the proposed VIM-FD achieves state-of-the-art performance.
arXiv admin note: text overlap with arXiv:1708.05237 and substantial text overlap with arXiv:1809.02693 by other authors
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- YOLO9000: Better, Faster, Stronger
- A Fast and Accurate Unconstrained Face Detector
- Face Attention Network: An Effective Face Detector for the Occluded Faces
- Scale-Aware Face Detection
Cited by in corpus (9)
- RetinaFace: Single-stage Dense Face Localisation in the Wild
- TinaFace: Strong but Simple Baseline for Face Detection
- Going Deeper Into Face Detection: A Survey
- LFFD: A Light and Fast Face Detector for Edge Devices
- YOLO5Face: Why Reinventing a Face Detector
- Improved Selective Refinement Network for Face Detection
- Accurate Face Detection for High Performance
- RefineFace: Refinement Neural Network for High Performance Face Detection
- Face Detection with Feature Pyramids and Landmarks