18 citations · 34 across the 7 of their papers we have counts for
14 papers
PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition
Pedro C. Neto, Ana F. Sequeira, Jaime S. Cardoso +1
In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct. Since many biometric systems operate in critical decision-maki…
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…
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…
Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
Philipp Terhörst, Marco Huber, Naser Damer +3
An essential factor to achieve high performance in face recognition systems is the quality of its samples. Since these systems are involved in daily life there is a strong need of…
QMagFace: Simple and Accurate Quality-Aware Face Recognition
Philipp Terhörst, Malte Ihlefeld, Marco Huber +4
Face recognition systems have to deal with large variabilities (such as different poses, illuminations, and expressions) that might lead to incorrect matching decisions. These vari…
An Attack on Facial Soft-biometric Privacy Enhancement
Dailé Osorio-Roig, Christian Rathgeb, Pawel Drozdowski +3
In the recent past, different researchers have proposed privacy-enhancing face recognition systems designed to conceal soft-biometric attributes at feature level. These works have…
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…
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…
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…