research-article

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

Authors:

Markos Georgopoulos,

James Oldfield,

Mihalis A. Nicolaou,

Yannis Panagakis,

Maja PanticAuthors Info & Claims

International Journal of Computer Vision, Volume 129, Issue 7

Pages 2288 - 2307

https://doi.org/10.1007/s11263-021-01448-w

Published: 01 July 2021 Publication History

Abstract

Deep learning has catalysed progress in tasks such as face recognition and analysis, leading to a quick integration of technological solutions in multiple layers of our society. While such systems have proven to be accurate by standard evaluation metrics and benchmarks, a surge of work has recently exposed the demographic bias that such algorithms exhibit–highlighting that accuracy does not entail fairness. Clearly, deploying biased systems under real-world settings can have grave consequences for affected populations. Indeed, learning methods are prone to inheriting, or even amplifying the bias present in a training set, manifested by uneven representation across demographic groups. In facial datasets, this particularly relates to attributes such as skin tone, gender, and age. In this work, we address the problem of mitigating bias in facial datasets by data augmentation. We propose a multi-attribute framework that can successfully transfer complex, multi-scale facial patterns even if these belong to underrepresented groups in the training set. This is achieved by relaxing the rigid dependence on a single attribute label, and further introducing a tensor-based mixing structure that captures multiplicative interactions between attributes in a multilinear fashion. We evaluate our method with an extensive set of qualitative and quantitative experiments on several datasets, with rigorous comparisons to state-of-the-art methods. We find that the proposed framework can successfully mitigate dataset bias, as evinced by extensive evaluations on established diversity metrics, while significantly improving fairness metrics such as equality of opportunity.

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cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 129, Issue 7

Jul 2021

279 pages

ISSN:0920-5691

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© The Author(s) 2021.

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Kluwer Academic Publishers

United States

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Published: 01 July 2021

Accepted: 20 February 2021

Received: 15 May 2020

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Erfanian MJagadish HAsudeh A(2024)Chameleon: Foundation Models for Fairness-Aware Multi-Modal Data Augmentation to Enhance Coverage of MinoritiesProceedings of the VLDB Endowment10.14778/3681954.368201417:11(3470-3483)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3682014
Kaate ISalminen JJung SRizun NRevina AJansen B(2024)Getting Emotional Enough: Analyzing Emotional Diversity in Deepfake AvatarsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685398(1-12)Online publication date: 13-Oct-2024
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Joshi IGrimmer MRathgeb CBusch CBremond FDantcheva A(2024)Synthetic Data in Human Analysis: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.336282146:7(4957-4976)Online publication date: 6-Feb-2024
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Liang KYin ZMin MLiu YMa ZGuo J(2024)Learning Dynamic Prototypes for Visual Pattern DebiasingInternational Journal of Computer Vision10.1007/s11263-023-01956-x132:5(1777-1799)Online publication date: 1-May-2024
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Yang NLuan XJia HHan ZLi XTang Y(2024)CCR: Facial Image Editing with Continuity, Consistency and ReversibilityInternational Journal of Computer Vision10.1007/s11263-023-01938-z132:4(1336-1349)Online publication date: 1-Apr-2024
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Shahbazi NLin YAsudeh AJagadish H(2023)Representation Bias in Data: A Survey on Identification and Resolution TechniquesACM Computing Surveys10.1145/358843355:13s(1-39)Online publication date: 13-Jul-2023
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