Beyond Identity: What Information Is Stored in Biometric Face Templates?

Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more information is stored within, such as demographics, image characteristics, and social traits. This threatens the user's privacy, since for many applications these templates are expected to be solely used for recognition purposes. Knowing the encoded information in face templates helps to develop bias-mitigating and privacy-preserving face recognition technologies. This work aims to support the development of these two branches by analysing face templates regarding 113 attributes. Experiments were conducted on two publicly available face embeddings. For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence. This allows us to make more sophisticated statements about the attribute predictability. The results demonstrate that up to 74 attributes can be accurately predicted from face templates. Especially non-permanent attributes, such as age, hairstyles, haircolors, beards, and various accessories, found to be easily-predictable. Since face recognition systems aim to be robust against these variations, future research might build on this work to develop more understandable privacy preserving solutions and build robust and fair face templates.Comment: To appear in IJCB 202

Datenschutz-Folgenabschätzung und Transparenzdefizite der Techniknutzung: Eine Untersuchung am Beispiel der polizeilichen Datenverarbeitungstechnologie

Seit 2018 ist auch für Datenverarbeitungsvorgänge der Polizei nach dem EU‑Datenschutzrecht bei hohen Risiken für die Rechte und Freiheiten der Betroffenen eine Datenschutz-Folgenabschätzung (DSFA) vorgeschrieben. Dieser Beitrag untersucht die Möglichkeiten, die diese verbindliche DSFA für eine transparente, grundrechtsschonende und demokratisch kontrollierbare Polizeiarbeit bietet. Er zeigt, dass sich viele Akteure der Innenpolitik und Polizei mit Transparenz schwertun, sodass eine demokratische Kontrolle, die grundrechtsschonendes Polizeihandeln sicherstellen soll, nur eingeschränkt funktioniert. Dem kann durch höhere Transparenzstandards bei der polizeilichen Datenverarbeitung sowie durch eine grundrechtsschonende Technikgestaltung nach dem Grundsatz Privacy by Design entgegengewirkt werden.Since 2018, EU data protection law requires a Data Protection Impact Assessment (DPIA) for any data processing that involves high risks to the rights and freedoms of natural persons. This paper examines the possibilities for transparent and fundamental rights-protecting policing that this legal framework offers. Many politicians and police officials tend to place more emphasis on security than on transparency, democratic accountability of policing, and high standards of privacy. This can be counteracted by higher transparency standards in police data processing and by designing technology based on privacy by design

A semiparametric approach for item response function estimation to detect item misfit

When scaling data using item response theory, valid statements based on the measurement model are only permissible if the model fits the data. Most item fit statistics used to assess the fit between observed item responses and the item responses predicted by the measurement model show significant weaknesses, such as the dependence of fit statistics on sample size and number of items. In order to assess the size of misfit and to thus use the fit statistic as an effect size, dependencies on properties of the data set are undesirable. The present study describes a new approach and empirically tests it for consistency. We developed an estimator of the distance between the predicted item response functions (IRFs) and the true IRFs by semiparametric adaptation of IRFs. For the semiparametric adaptation, the approach of extended basis functions due to Ramsay and Silverman (2005) is used. The IRF is defined as the sum of a linear term and a more flexible term constructed via basis function expansions. The group lasso method is applied as a regularization of the flexible term, and determines whether all parameters of the basis functions are fixed at zero or freely estimated. Thus, the method serves as a selection criterion for items that should be adjusted semiparametrically. The distance between the predicted and semiparametrically adjusted IRF of misfitting items can then be determined by describing the fitting items by the parametric form of the IRF and the misfitting items by the semiparametric approach. In a simulation study, we demonstrated that the proposed method delivers satisfactory results in large samples (i.e., N ≥ 1,000). (DIPF/Orig.

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

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.Comment: Accepted in IEEE Transactions on Information Forensics and Securit

Eingriffsintensive Personenkontrollen – Eine Gegenüberstellung von Gerichtsentscheidungen und empirischer Praxis

Polizeiliche Personenkontrollen umfassen regelmäßig ein Maßnahmenbündel: Identitätsfeststellung, Befragung/Vernehmung, Datenbankabgleich, Durchsuchung und/oder Mitnahme zur Wache. In der gerichtlichen Sachverhaltsdarstellungen (Tatbeständen) fehlen aber zahlreiche Maßnahmen: Befragungen und Datenbankabgleiche werden beispielsweise nicht oder kaum beschrieben. Soziologische Untersuchung verdeutlichen, dass Abläufe bei Personenkontrollen zahlreihe mögliche (teilweise ungewollte) stigmatisierende, diskriminierende und disruptive Effekte entfalten können, die, aus einer rechtswissenschaftlichen Perspektive, zu einer hohen Eingriffsintensität der Maßnahmen führen können. In der gerichtlichen Praxis werden diese Effekte und die damit verbundenen mögliche Steigerung der Eingriffsintensität aber nicht oder kaum thematisiert und wird regelmäßig eine niedrige Eingriffsintensität der Maßnahmen während einer Personenkontrolle angenommen. Im Rahmen dieses Beitrages werden die Abläufe während Personenkontrollen soziologisch untersucht und rechtsdogmatisch hergeleitet, wann und warum die Eingriffe als intensiv einzustufen sind