18 citations · 34 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
Philipp Terhörst, Jan Niklas Kolf, Naser Damer +2
Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently,…
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Philipp Terhörst, Jan Niklas Kolf, Naser Damer +2
Face image quality is an important factor to enable high performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for recogn…
Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition
Philipp Terhörst, Marco Huber, Naser Damer +2
Current research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric templates of an individual. Since for many applications, these templates…
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
Philipp Terhörst, Jan Niklas Kolf, Naser Damer +2
Current face recognition systems achieve high progress on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demogra…