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。

相对于种族特征的人脸识别准确性变异的特征描述