Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society. In this study, we propose a novel adversarial derived data augmentation methodology that aims to enable dataset balance at a per-subject level via the use of image-to-image transformation for the transfer of sensitive racial characteristic facial features. Our aim is to automatically construct a synthesised dataset by transforming facial images across varying racial domains, while still preserving identity-related features, such that racially dependant features subsequently become irrelevant within the determination of subject identity. We construct our experiments on three significant face recognition variants: Softmax, CosFace and ArcFace loss over a common convolutional neural network backbone. In a side-by-side comparison we show the positive impact our proposed technique can have on the recognition performance for (racial) minority groups within an originally imbalanced training dataset by reducing the per-race variance in performance.
The generalisation of face recognition research and applications is problematic due to the prevalence of bias occurrences within face recognition. The imbalance in specific demographic groups occurring with varying geographic locale globally, including race, age or gender, poses a challenge of transparent explanations and solutions for facial recognition applications. Our approach is based on adversarial image synthesise to mitigate bias. We transform race information from one group to another for fair face recognition. We aim to augment sensitive attributes to make them irrelevant for face recognition solutions.
If you are making use of this work in any way (including our pre-trained models or datasets), you must please reference the following articles in any report, publication, presentation, software release or any other associated materials: Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2020.
@inproceedings{yucer2020exploring,
title={Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation},
author={Yucer, Seyma and Ak{\c{c}}ay, Samet and Al-Moubayed, Noura and Breckon, Toby P},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
pages={18--19},
year={2020}
}