3D Mask Presentation Attack Detection via High Resolution Face Parts

Oleg Grinchuk, Aleksandr Parkin, Evgenija Glazistova

ICCVW 2021 pp. 846-853

doi:10.1109/ICCVW54120.2021.00100 /iccvw/2021/grinchuk2021iccvw-3d/

Abstract

3D mask presentation attack detection (PAD) is a long standing challenge in face anti-spoofing due to the high fidelity of attack artifacts and a limited number of samples available for training and evaluation. With the recent release of the large-scale and diverse CASIA-SURF HiFiMask dataset [19], it now becomes possible to address 3D mask PAD with deep neural networks. This paper introduces a new one-shot method for 3D mask PAD that extracts fine-grained information from appropriate parts of the human face and uses it to identify subtle differences between real and fake samples. The proposed method achieves state-of-the-art results of 3% ACER on the CASIA-SURF HiFiMask test set.

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Cite

Text

Grinchuk et al. "3D Mask Presentation Attack Detection via High Resolution Face Parts." IEEE/CVF International Conference on Computer Vision Workshops, 2021. doi:10.1109/ICCVW54120.2021.00100

Markdown

[Grinchuk et al. "3D Mask Presentation Attack Detection via High Resolution Face Parts." IEEE/CVF International Conference on Computer Vision Workshops, 2021.](https://mlanthology.org/iccvw/2021/grinchuk2021iccvw-3d/) doi:10.1109/ICCVW54120.2021.00100

BibTeX

@inproceedings{grinchuk2021iccvw-3d,
  title     = {{3D Mask Presentation Attack Detection via High Resolution Face Parts}},
  author    = {Grinchuk, Oleg and Parkin, Aleksandr and Glazistova, Evgenija},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2021},
  pages     = {846-853},
  doi       = {10.1109/ICCVW54120.2021.00100},
  url       = {https://mlanthology.org/iccvw/2021/grinchuk2021iccvw-3d/}
}