Many recent news headlines have labeled face recognition technology as biased
or racist. We report on a methodical investigation into differences in face
recognition accuracy between African-American and Caucasian image cohorts of
the MORPH dataset. We find that, for all four matchers considered, the impostor
and the genuine distributions are statistically significantly different between
cohorts. For a fixed decision threshold, the African-American image cohort has
a higher false match rate and a lower false non-match rate. ROC curves compare
verification rates at the same false match rate, but the different cohorts
achieve the same false match rate at different thresholds. This means that ROC
comparisons are not relevant to operational scenarios that use a fixed decision
threshold. We show that, for the ResNet matcher, the two cohorts have
approximately equal separation of impostor and genuine distributions. Using
ICAO compliance as a standard of image quality, we find that the initial image
cohorts have unequal rates of good quality images. The ICAO-compliant subsets
of the original image cohorts show improved accuracy, with the main effect
being to reducing the low-similarity tail of the genuine distributions.

该研究对 MORPH 数据集中的非裔美国人和白人图像组之间的面部识别准确性差异进行了系统调查，发现四个匹配器的假冒者和真实分布在组之间存在显着差异，使用 ICAO 合规性作为图像质量标准，结果发现初始图像组具有不均等的合格图像比率，并表明该 ResNet 匹配器的效果更好。