  The fairness of biometric systems, in particular facial recognition, is often
analysed for larger demographic groups, e.g. female vs. male or black vs.
white. In contrast to this, minority groups are commonly ignored. This paper
investigates the performance of facial recognition algorithms on individuals
with Down syndrome, a common chromosomal abnormality that affects approximately
one in 1,000 births per year. To do so, a database of 98 individuals with Down
syndrome, each represented by at least five facial images, is
semi-automatically collected from YouTube. Subsequently, two facial image
quality assessment algorithms and five recognition algorithms are evaluated on
the newly collected database and on the public facial image databases CelebA
and FRGCv2. The results show that the quality scores of facial images for
individuals with Down syndrome are comparable to those of individuals without
Down syndrome captured under similar conditions. Furthermore, it is observed
that face recognition performance decreases significantly for individuals with
Down syndrome, which is largely attributed to the increased likelihood of false
matches.
