Attributes Preserving Face De-Identification
Abstract
In this paper, we propose a Face de-identification method to remove the identification information of a person while maintaining all the face attributes such as expression, age and gender. Motivated by the k-Same algorithm, our method consists of three steps: first, k face images are selected randomly. These k face images may contain same or different face attributes with the test face image. Secondly, ELEGANT model is employed to transfer attributes from the test face to the k selected faces. After attributes transferring, the k selected faces have the same attributes as the test face. Then we average the k selected faces as the de-identified image of the test face. Experimental results show that our method can de-identify a face image while preserving all of its attributes effectively.
Cite
Text
Yan et al. "Attributes Preserving Face De-Identification." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00154Markdown
[Yan et al. "Attributes Preserving Face De-Identification." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/yan2019iccvw-attributes/) doi:10.1109/ICCVW.2019.00154BibTeX
@inproceedings{yan2019iccvw-attributes,
title = {{Attributes Preserving Face De-Identification}},
author = {Yan, Bin and Pei, Mingtao and Nie, Zhengang},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {1217-1221},
doi = {10.1109/ICCVW.2019.00154},
url = {https://mlanthology.org/iccvw/2019/yan2019iccvw-attributes/}
}