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Does lossy image compression affect racial bias within face recognition?

Yucer, S.; Poyser, M.; Al Moubayed, N.; Breckon, T.P.

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This study investigates the impact of commonplace lossy image compression on face recognition algorithms with regard to the racial characteristics of the subject. We adopt a recently proposed racial phenotype-based bias analysis methodology to measure the effect of varying levels of lossy compression across racial phenotype categories. Additionally, we determine the relationship between chroma-subsampling and race-related phenotypes for recognition performance. Prior work investigates the impact of lossy JPEG compression algorithm on contemporary face recognition performance. However, there is a gap in how this impact varies with different race-related inter-sectional groups and the cause of this impact. Via an extensive experimental setup, we demonstrate that common lossy image compression approaches have a more pronounced negative impact on facial recognition performance for specific racial phenotype categories such as darker skin tones (by up to 34.55%). Furthermore, removing chromasubsampling during compression improves the false matching rate (up to 15.95%) across all phenotype categories affected by the compression, including darker skin tones, wide noses, big lips, and monolid eye categories. In addition, we outline the characteristics that may be attributable as the underlying cause of such phenomenon for lossy compression algorithms such as JPEG.


Yucer, S., Poyser, M., Al Moubayed, N., & Breckon, T. (2022). Does lossy image compression affect racial bias within face recognition?.

Conference Name International Joint Conference on Biometrics (IJCB 2022)
Conference Location Abu Dhabi, UAE
Start Date Oct 10, 2022
End Date Oct 13, 2022
Acceptance Date Jul 25, 2022
Publication Date 2022-10
Deposit Date Aug 18, 2022
Publicly Available Date Oct 14, 2022
Publisher Institute of Electrical and Electronics Engineers
Publisher URL


Accepted Conference Proceeding (883 Kb)

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