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

Terhorst, Philipp ; Fahrmann, Daniel ; Kolf, Jan Niklas ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan (2021):
MAAD-Face: A Massively Annotated Attribute Dataset for Face Images.
In: IEEE Transactions on Information Forensics and Security, (Early Access), IEEE, ISSN 1556-6013,
DOI: 10.1109/TIFS.2021.3096120,
[Article]

Abstract

Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threaten 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 a large number of face images but lack in the number of attribute annotations and the overall annotation correctness. In this work, we propose a novel annotation-transfer pipeline that allows to accurately transfer attribute annotations from multiple source datasets to a target dataset. The transfer is based on a massive attribute classifier that can accurately state its prediction confidence. Using these prediction confidences, a high correctness of the transferred annotations is ensured. Applying this pipeline to the VGGFace2 database, we propose the MAAD-Face annotation database. It consists of 3.3M faces of over 9k individuals and provides 123.9M attribute annotations of 47 different binary attributes. Consequently, it provides 15 and 137 times more attribute annotations 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 number of high-quality annotations from MAAD-Face to study the viability of soft-biometrics for recognition, providing insights into which attributes support genuine and imposter decisions. The MAAD-Face annotations dataset is publicly available.

Item Type: Article
Erschienen: 2021
Creators: Terhorst, Philipp ; Fahrmann, Daniel ; Kolf, Jan Niklas ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan
Title: MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
Language: English
Abstract:

Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threaten 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 a large number of face images but lack in the number of attribute annotations and the overall annotation correctness. In this work, we propose a novel annotation-transfer pipeline that allows to accurately transfer attribute annotations from multiple source datasets to a target dataset. The transfer is based on a massive attribute classifier that can accurately state its prediction confidence. Using these prediction confidences, a high correctness of the transferred annotations is ensured. Applying this pipeline to the VGGFace2 database, we propose the MAAD-Face annotation database. It consists of 3.3M faces of over 9k individuals and provides 123.9M attribute annotations of 47 different binary attributes. Consequently, it provides 15 and 137 times more attribute annotations 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 number of high-quality annotations from MAAD-Face to study the viability of soft-biometrics for recognition, providing insights into which attributes support genuine and imposter decisions. The MAAD-Face annotations dataset is publicly available.

Journal or Publication Title: IEEE Transactions on Information Forensics and Security
Number: Early Access
Publisher: IEEE
Uncontrolled Keywords: Biometrics, Face recognition, Deep learning, Machine learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 13 Jul 2021 08:40
DOI: 10.1109/TIFS.2021.3096120
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