ML Anthology

Live Face De-Identification in Video

Oran Gafni, Lior Wolf, Yaniv Taigman
ICCV 2019
doi:10.1109/ICCV.2019.00947 /iccv/2019/gafni2019iccv-live/

Abstract

We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed-forward encoder-decoder network architecture that is conditioned on the high-level representation of a person's facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural looking image sequences with little distortion in time.

PDF ICCV Semantic Scholar

Cite

Text

Gafni et al. "Live Face De-Identification in Video." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00947

Markdown

[Gafni et al. "Live Face De-Identification in Video." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/gafni2019iccv-live/) doi:10.1109/ICCV.2019.00947

BibTeX

@inproceedings{gafni2019iccv-live,
  title     = {{Live Face De-Identification in Video}},
  author    = {Gafni, Oran and Wolf, Lior and Taigman, Yaniv},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year      = {2019},
  doi       = {10.1109/ICCV.2019.00947},
  url       = {https://mlanthology.org/iccv/2019/gafni2019iccv-live/}
}