Search Results for author: Jan Niklas Kolf

Found 12 papers, 9 papers with code

GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes

1 code implementation18 Apr 2024 Jan Niklas Kolf, Naser Damer, Fadi Boutros

We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset.

Face Image Quality Face Image Quality Assessment +1

Liveness Detection Competition -- Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)

no code implementations1 Oct 2023 Sandip Purnapatra, Humaira Rezaie, Bhavin Jawade, Yu Liu, Yue Pan, Luke Brosell, Mst Rumana Sumi, Lambert Igene, Alden Dimarco, Srirangaraj Setlur, Soumyabrata Dey, Stephanie Schuckers, Marco Huber, Jan Niklas Kolf, Meiling Fang, Naser Damer, Banafsheh Adami, Raul Chitic, Karsten Seelert, Vishesh Mistry, Rahul Parthe, Umit Kacar

The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones.

EFaR 2023: Efficient Face Recognition Competition

2 code implementations8 Aug 2023 Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen, Markus Theuerkauf, Naser Damer, Mohamad Alansari, Oussama Abdul Hay, Sara Alansari, Sajid Javed, Naoufel Werghi, Klemen Grm, Vitomir Štruc, Fernando Alonso-Fernandez, Kevin Hernandez Diaz, Josef Bigun, Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza, Ketan Kotwal, Sébastien Marcel, Iurii Medvedev, Bo Jin, Diogo Nunes, Ahmad Hassanpour, Pankaj Khatiwada, Aafan Ahmad Toor, Bian Yang

To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.

Lightweight Face Recognition Quantization

Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition

2 code implementations30 Apr 2023 Jan Niklas Kolf, Tim Rieber, Jurek Elliesen, Fadi Boutros, Arjan Kuijper, Naser Damer

We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation.

Generative Adversarial Network Synthetic Face Recognition

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

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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