Joint Cascade Face Detection and Alignment
Abstract
We present a new state-of-the-art approach for face detection. The key idea is to combine face alignment with detection, observing that aligned face shapes provide better features for face classification. To make this combination more effective, our approach learns the two tasks jointly in the same cascade framework, by exploiting recent advances in face alignment. Such joint learning greatly enhances the capability of cascade detection and still retains its realtime performance. Extensive experiments show that our approach achieves the best accuracy on challenging datasets, where all existing solutions are either inaccurate or too slow.
Cite
Text
Chen et al. "Joint Cascade Face Detection and Alignment." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10599-4_8Markdown
[Chen et al. "Joint Cascade Face Detection and Alignment." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/chen2014eccv-joint/) doi:10.1007/978-3-319-10599-4_8BibTeX
@inproceedings{chen2014eccv-joint,
title = {{Joint Cascade Face Detection and Alignment}},
author = {Chen, Dong and Ren, Shaoqing and Wei, Yichen and Cao, Xudong and Sun, Jian},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {109-122},
doi = {10.1007/978-3-319-10599-4_8},
url = {https://mlanthology.org/eccv/2014/chen2014eccv-joint/}
}