  This paper is the first to explore the question of whether images that are
classified incorrectly by a face analytics algorithm (e.g., gender
classification) are any more or less likely to participate in an image pair
that results in a face recognition error. We analyze results from three
different gender classification algorithms (one open-source and two
commercial), and two face recognition algorithms (one open-source and one
commercial), on image sets representing four demographic groups
(African-American female and male, Caucasian female and male). For impostor
image pairs, our results show that pairs in which one image has a gender
classification error have a better impostor distribution than pairs in which
both images have correct gender classification, and so are less likely to
generate a false match error. For genuine image pairs, our results show that
individuals whose images have a mix of correct and incorrect gender
classification have a worse genuine distribution (increased false non-match
rate) compared to individuals whose images all have correct gender
classification. Thus, compared to images that generate correct gender
classification, images that generate gender classification errors do generate a
different pattern of recognition errors, both better (false match) and worse
(false non-match).
