Most studies to date that have examined demographic variations in face recognition accuracy have analyzed 1-to-1 matching accuracy, using images that could be described as "government ID quality". This paper analyzes the accuracy of 1-to-many facial identification across demographic groups, and in the presence of blur and reduced resolution in the probe image as might occur in "surveillance camera quality" images. Cumulative match characteristic curves (CMC) are not appropriate for comparing propensity for rank-one recognition errors across demographics, and so we use three metrics for our analysis: (1) the well-known d' metric between mated and non-mated score distributions, and introduced in this work, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation in 1-to-many accuracy does not entirely follow what has been observed in 1-to-1 matching accuracy. Also, different from 1-to-1 accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. More importantly, we show that increased blur in the probe image, or reduced resolution of the face in the probe image, can significantly increase the false positive identification rate. And we show that the demographic variation in these high blur or low resolution conditions is much larger for male / female than for African-American / Caucasian. The point that 1-to-many accuracy can potentially collapse in the context of processing "surveillance camera quality" probe images against a "government ID quality" gallery is an important one.

翻译：目前多数关于人脸识别准确率人口统计学差异的研究均基于可称为“政府身份证质量”的图像，分析一对一的匹配精度。本文研究在探针图像存在模糊及分辨率降低（可能出现在“监控摄像头质量”图像中）时，一对多人脸识别在不同人口群体间的准确性。累积匹配特征曲线（CMC）不适合比较跨人口群体的首位识别错误倾向，因此我们采用三种指标进行分析：（1）已知的配对与非配对分数分布间的d'指标；（2）本文引入的非配对高分尾部与配对低分尾部阈值间的绝对分数差；（3）探针图像集上（配对分数减去非配对首位分数）的分布。我们发现一对多精度的人口统计学变异并不完全遵循一对一的观测结果。此外，与一对一的精度不同，不同人口群体间身份与图像数量的差异会影响一对多精度的人口统计学比较。更重要的是，研究表明探针图像中模糊程度的增加或面部分辨率的降低会显著提高误报识别率。同时我们发现，在高模糊或低分辨率条件下，性别（男/女）间的人口统计学差异远大于种族（非裔美国人/高加索人）。一个关键结论是：当使用“监控摄像头质量”探针图像与“政府身份证质量”图库进行匹配时，一对多识别精度可能显著下降。