  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.
