  Soft-biometrics play an important role in face biometrics and related fields
since these might lead to biased performances, threatens the user's privacy, or
are valuable for commercial aspects. Current face databases are specifically
constructed for the development of face recognition applications. Consequently,
these databases contain large amount of face images but lack in the number of
attribute annotations and the overall annotation correctness. In this work, we
propose MAADFace, a new face annotations database that is characterized by the
large number of its high-quality attribute annotations. MAADFace is build on
the VGGFace2 database and thus, consists of 3.3M faces of over 9k individuals.
Using a novel annotation transfer-pipeline that allows an accurate
label-transfer from multiple source-datasets to a target-dataset, MAAD-Face
consists of 123.9M attribute annotations of 47 different binary attributes.
Consequently, it provides 15 and 137 times more attribute labels than CelebA
and LFW. Our investigation on the annotation quality by three human evaluators
demonstrated the superiority of the MAAD-Face annotations over existing
databases. Additionally, we make use of the large amount of high-quality
annotations from MAAD-Face to study the viability of soft-biometrics for
recognition, providing insights about which attributes support genuine and
imposter decisions. The MAAD-Face annotations dataset is publicly available.
