ArcFace for Disguised Face Recognition
Abstract
Even though deep face recognition is extensively explored and remarkable advances have been achieved on large-scale in-the-wild dataset, disguised face recognition receives much less attention. Face feature embedding targeting on intra-class compactness and inter-class discrepancy is very challenging as high intra-class diversity and inter-class similarity are very common on the disguised face recognition dataset. In this report, we give the technical details of our submission to the DFW2019 challenge. By using our RetinaFace for face detection and alignment and ArcFace for face feature embedding, we achieve state-of-the-art performance on the DFW2019 challenge.
Cite
Text
Deng and Zafeiriou. "ArcFace for Disguised Face Recognition." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00061Markdown
[Deng and Zafeiriou. "ArcFace for Disguised Face Recognition." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/deng2019iccvw-arcface/) doi:10.1109/ICCVW.2019.00061BibTeX
@inproceedings{deng2019iccvw-arcface,
title = {{ArcFace for Disguised Face Recognition}},
author = {Deng, Jiankang and Zafeiriou, Stefanos},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {485-493},
doi = {10.1109/ICCVW.2019.00061},
url = {https://mlanthology.org/iccvw/2019/deng2019iccvw-arcface/}
}