activity
20202022
most citedUnsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition

18 citations · 26 across the 3 of their papers we have counts for

collaborators

10 papers

cs.CV20227 cited

Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition

Marco Huber, Philipp Terhörst, Florian Kirchbuchner +2

Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a dec…

cs.CV20221 cited

On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations

Philipp Terhörst, Florian Bierbaum, Marco Huber +4

A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolle…

cs.CV2021

MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence

Philipp Terhörst, André Boller, Naser Damer +2

An essential factor to achieve high accuracies in fingerprint recognition systems is the quality of its samples. Previous works mainly proposed supervised solutions based on image…

cs.CV2021

A Comprehensive Study on Face Recognition Biases Beyond Demographics

Philipp Terhörst, Jan Niklas Kolf, Marco Huber +5

Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on th…

cs.CV2020

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

Philipp Terhörst, Daniel Fährmann, Jan Niklas Kolf +3

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 commer…

cs.CV2020

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

Philipp Terhörst, Daniel Fährmann, Naser Damer +2

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, re…