2020/11/26 by Yanjia Zhu, Zhu, Yanjia, Hongxiang Cai +7 · 1 voice · 2 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.2011.13183
openalex publication_date 2020/11/26 · arxiv published 2020/11/26 · arxiv updated 2021/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Face detection has received intensive attention in recent years. Many works present lots of special methods for face detection from different perspectives like model architecture, data augmentation, label assignment and etc., which make the overall algorithm and system become more and more complex. In this paper, we point out that there is no gap between face detection and generic object detection. Then we provide a strong but simple baseline method to deal with face detection named TinaFace. We use ResNet-50 \citehe2016deep as backbone, and all modules and techniques in TinaFace are constructed on existing modules, easily implemented and based on generic object detection. On the hard test set of the most popular and challenging face detection benchmark WIDER FACE \citeyang2016wider, with single-model and single-scale, our TinaFace achieves 92.1% average precision (AP), which exceeds most of the recent face detectors with larger backbone. And after using test time augmentation (TTA), our TinaFace outperforms the current state-of-the-art method and achieves 92.4% AP. The code will be available at \urlhttps://github.com/Media-Smart/vedadet.