Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data

In this work, we demonstrate the large potential of synthetic data for analyzing and reducing the negative effects of dataset bias on deep face recognition systems.

Released in: Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data

Contributor:

by yashar

Homepage

Summary

In this work, we demonstrate the large potential of synthetic data for analyzing and reducing the negative effects of dataset bias on deep face recognition systems.

200

Images in dataset

2019

Year Released

Key Links & Stats

A. Kortylewski, B. Egger, A. Schneider, T. Gerig, A. Morel-Forster and T. Vetter, "Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2261-2268, doi: 10.1109/CVPRW.2019.00279.

Modalities

Still Image

Verticals

Satellite

ML Task

Bias Identification