  Recent news articles have accused face recognition of being "biased",
"sexist" or "racist". There is consensus in the research literature that face
recognition accuracy is lower for females, who often have both a higher false
match rate and a higher false non-match rate. However, there is little
published research aimed at identifying the cause of lower accuracy for
females. For instance, the 2019 Face Recognition Vendor Test that documents
lower female accuracy across a broad range of algorithms and datasets also
lists "Analyze cause and effect" under the heading "What we did not do". We
present the first experimental analysis to identify major causes of lower face
recognition accuracy for females on datasets where previous research has
observed this result. Controlling for equal amount of visible face in the test
images reverses the apparent higher false non-match rate for females. Also,
principal component analysis indicates that images of two different females are
inherently more similar than of two different males, potentially accounting for
a difference in false match rates.
