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

MAAD-Face: A Massively Annotated Attribute Dataset for Face Images