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：迄今为止，大多数关于人脸识别准确性人口统计差异的研究均基于"政府证件照质量"图像进行一对一的匹配准确性分析。本文分析了在探针图像存在模糊和分辨率降低（可能出现在"监控摄像头质量"图像中）的情况下，不同人口统计群体的一对多人脸识别准确性。累积匹配特征曲线（CMC）不适用于比较不同人口群体间排名第一识别错误的倾向性，因此我们采用三个指标进行分析：（1）匹配与非匹配分数分布之间著名的d'指标（本文引入）；（2）非匹配分布高相似度尾部与匹配分布低相似度尾部之间阈值的绝对分数差异；（3）探针图像集中（匹配与排名第一的非匹配分数之差）的分布。我们发现，一对多识别准确率的人口统计差异并不完全遵循一对一匹配准确率的观察结果。此外，与一对一准确性不同，一对多准确率的人口统计比较可能受到不同人口群体身份数量和图像数量的影响。更重要的是，我们表明探针图像中模糊度的增加或面部分辨率的降低会显著提高误识率。同时我们发现，在这些高模糊或低分辨率条件下，男/女群体间的人口统计差异远大于非裔美国人/高加索群体间的差异。在将"监控摄像头质量"探针图像与"政府证件照质量"图库进行匹配处理时，一对多识别准确性可能显著下降这一结论具有重要实践意义。