Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power. This paper proposes a one-stage method named CenterFace to simultaneously predict facial box and landmark location with real-time speed and high accuracy. The proposed method also belongs to the anchor free category. This is achieved by: (a) learning face existing possibility by the semantic maps, (b) learning bounding box, offsets and five landmarks for each position that potentially contains a face. Specifically, the method can run in real-time on a single CPU core and 200 FPS using NVIDIA 2080TI for VGA-resolution images, and can simultaneously achieve superior accuracy (WIDER FACE Val/Test-Easy: 0.935/0.932, Medium: 0.924/0.921, Hard: 0.875/0.873 and FDDB discontinuous: 0.980, continuous: 0.732). A demo of CenterFace can be available at https://github.com/Star-Clouds/CenterFace.
@article{arxiv.1911.03599,
title = {CenterFace: Joint Face Detection and Alignment Using Face as Point},
author = {Yuanyuan Xu and Wan Yan and Haixin Sun and Genke Yang and Jiliang Luo},
journal= {arXiv preprint arXiv:1911.03599},
year = {2019}
}
Comments
11 pages, 3 figures. A demo of CenterFace can be available at https://github.com/Star-Clouds/CenterFace