The Hitchhiker's Guide to Bias and Fairness in Facial Affective Signal Processing: Overview and techniques

Date

2021-11-01

Author

Cheong, Jiaee
Kalkan, Sinan
Gunes, Hatice

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Given the increasing prevalence of facial analysis technology, the problem of bias in the tools is now becoming an even greater source of concern. Several studies have highlighted the pervasiveness of such discrimination, and many have sought to address the problem by proposing solutions to mitigate it. Despite this effort, to date, understanding, investigating, and mitigating bias for facial affect analysis remain an understudied problem. In this work we aim to provide a guide by 1) providing an overview of the various definitions of bias and measures of fairness within the field of facial affective signal processing and 2) categorizing the algorithms and techniques that can be used to investigate and mitigate bias in facial affective signal processing. We present the opportunities and limitations within the current body of work, discuss the gathered findings, and propose areas that call for further research.

URI

https://hdl.handle.net/11511/94545

Journal

IEEE SIGNAL PROCESSING MAGAZINE

DOI

https://doi.org/10.1109/msp.2021.3106619

Collections

Department of Computer Engineering, Article

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Citation Formats

J. Cheong, S. Kalkan, and H. Gunes, “The Hitchhiker’s Guide to Bias and Fairness in Facial Affective Signal Processing: Overview and techniques,” IEEE SIGNAL PROCESSING MAGAZINE, vol. 38, no. 6, pp. 39–49, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94545.