Search Results for author: Philipp Terhörst

Found 16 papers, 11 papers with code

Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation

1 code implementation26 Apr 2023 Marco Huber, Anh Thi Luu, Philipp Terhörst, Naser Damer

Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications.

Face Recognition Face Verification

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

3 code implementations11 Aug 2022 Ying Xu, Philipp Terhörst, Kiran Raja, Marius Pedersen

In this work, we investigate factors causing biased detection in public Deepfake datasets by (a) creating large-scale demographic and non-demographic attribute annotations with 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing attributes resulting in AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets.

Attribute Decision Making +3

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

no code implementations23 Mar 2022 Philipp Terhörst, Florian Bierbaum, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper

However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data.

Face Recognition Fairness

QMagFace: Simple and Accurate Quality-Aware Face Recognition

1 code implementation26 Nov 2021 Philipp Terhörst, Malte Ihlefeld, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper

These variabilities can be measured in terms of face image quality which is defined over the utility of a sample for recognition.

Face Image Quality Face Recognition +1

An Attack on Facial Soft-biometric Privacy Enhancement

no code implementations24 Nov 2021 Dailé Osorio-Roig, Christian Rathgeb, Pawel Drozdowski, Philipp Terhörst, Vitomir Štruc, Christoph Busch

Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection.

Attribute Dimensionality Reduction +1

Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

1 code implementation21 Oct 2021 Philipp Terhörst, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper

To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model.

Face Image Quality Face Image Quality Assessment +1

A Comprehensive Study on Face Recognition Biases Beyond Demographics

no code implementations2 Mar 2021 Philipp Terhörst, Jan Niklas Kolf, Marco Huber, Florian Kirchbuchner, Naser Damer, Aythami Morales, Julian Fierrez, Arjan Kuijper

However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics.

Attribute Decision Making +1

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

1 code implementation2 Dec 2020 Philipp Terhörst, Daniel Fährmann, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner, Arjan Kuijper

In this work, we propose MAADFace, a new face annotations database that is characterized by the large number of its high-quality attribute annotations.

Attribute Face Recognition

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

no code implementations21 Sep 2020 Philipp Terhörst, Daniel Fährmann, Naser Damer, Florian Kirchbuchner, Arjan Kuijper

For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence.

Attribute Face Recognition +1

Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition

1 code implementation21 Feb 2020 Philipp Terhörst, Marco Huber, Naser Damer, Florian Kirchbuchner, Arjan Kuijper

Current research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric templates of an individual.

Face Recognition

Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization

1 code implementation10 Feb 2020 Philipp Terhörst, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner, Arjan Kuijper

In contrast to previous works, our fair normalization approach enhances the overall performance by up to 53. 2% at false match rate of 0. 001 and up to 82. 9% at a false match rate of 0. 00001.

Face Recognition Fairness

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