Fair Face Recognition Using Data Balancing, Enhancement and Fusion
Abstract
Racial bias is an important issue in biometrics, while has not been thoroughly studied in deep face recognition. By reducing the influence of gender and skin colour, this paper proposes a fair face recognition system with low bias. First, multiple preprocessing methods are added to improve the dual shot face detector for obtaining target face from a given test image. Then, a data re-sampling approach is employed to balance the data distribution and reduce the bias based on the analysis of training data. Moreover, multiple data enhancement methods are used to increase the accuracy performance. Finally, a linear-combination strategy is adopted to benefit from mutil-model fusion. ChaLearn Looking at People Fair Face Recognition challenge is supported by ECCV 2020. Our team (ustc-nelslip) ranked 1st in the development stage and 2nd in the test stage of this challenge. The code is available at https://github.com/HaoSir/ECCV-2020-Fair-Face-Recognition-challenge_2nd_place_solution-ustc-nelslip- .
Cite
Text
Yu et al. "Fair Face Recognition Using Data Balancing, Enhancement and Fusion." European Conference on Computer Vision Workshops, 2020. doi:10.1007/978-3-030-65414-6_34Markdown
[Yu et al. "Fair Face Recognition Using Data Balancing, Enhancement and Fusion." European Conference on Computer Vision Workshops, 2020.](https://mlanthology.org/eccvw/2020/yu2020eccvw-fair/) doi:10.1007/978-3-030-65414-6_34BibTeX
@inproceedings{yu2020eccvw-fair,
title = {{Fair Face Recognition Using Data Balancing, Enhancement and Fusion}},
author = {Yu, Jun and Hao, Xinlong and Xie, Haonian and Yu, Ye},
booktitle = {European Conference on Computer Vision Workshops},
year = {2020},
pages = {492-505},
doi = {10.1007/978-3-030-65414-6_34},
url = {https://mlanthology.org/eccvw/2020/yu2020eccvw-fair/}
}