  Yes - 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 chroma-subsampling 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.
