J
Jan Niklas Kolf
Researcher at Technische Universität Darmstadt
Publications - 19
Citations - 345
Jan Niklas Kolf is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 8, co-authored 14 publications receiving 126 citations. Previous affiliations of Jan Niklas Kolf include Fraunhofer Society.
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Proceedings ArticleDOI
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
TL;DR: Zhang et al. as mentioned in this paper proposed a novel concept to measure face quality based on an arbitrary face recognition model by determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated.
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SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
TL;DR: This work proposes a novel concept to measure face quality based on an arbitrary face recognition model that avoids the training phase completely and further outperforms all baseline approaches by a large margin.
Proceedings ArticleDOI
MFR 2021: Masked Face Recognition Competition
Fadi Boutros,Naser Damer,Jan Niklas Kolf,Kiran B. Raja,Florian Kirchbuchner,Raghavendra Ramachandra,Arjan Kuijper,Pengcheng Fang,Chao Zhang,Fei Wang,David Montero,Naiara Aginako,Basilio Sierra,Marcos Nieto,Mustafa Ekrem Erakin,Ugur Demir,Hazim Kemal Ekenel,Asaki Kataoka,Kohei Ichikawa,Shizuma Kubo,Jie Zhang,Mingjie He,Dan Han,Shiguang Shan,Klemen Grm,Vitomir Struc,Sachith Seneviratne,Nuran Kasthuriarachchi,Sanka Rasnayaka,Pedro C. Neto,Ana F. Sequeira,Joao Ribeiro Pinto,Mohsen Saffari,Jaime S. Cardoso +33 more
TL;DR: The Masked Face Recognition Competition (MFR) as discussed by the authors was held within the 2021 International Joint Conference on Biometrics (IJCB 2021) and attracted a total of 10 participating teams with valid submissions.
Journal ArticleDOI
Post-comparison mitigation of demographic bias in face recognition using fair score normalization
Philipp Terhorst,Philipp Terhorst,Jan Niklas Kolf,Naser Damer,Naser Damer,Florian Kirchbuchner,Florian Kirchbuchner,Arjan Kuijper,Arjan Kuijper +8 more
TL;DR: In this paper, an unsupervised fair score normalization approach was proposed to reduce the effect of bias in face recognition and subsequently lead to a significant overall performance boost, achieving up to 53.2% at false match rate of 10 − 3 and up to 82.7% in the case when gender is considered.
Proceedings ArticleDOI
Reliable Age and Gender Estimation from Face Images: Stating the Confidence of Model Predictions
Philipp Terhorst,Marco Huber,Jan Niklas Kolf,Ines Zelch,Naser Damer,Florian Kirchbuchner,Arjan Kuijper +6 more
TL;DR: This work proposes an age and gender estimation model, as well as a novel reliability measure to quantify the confidence of the model’s prediction, based on stochastic forward passes through dropout-reduced neural networks that were theoretically proven to approximate Gaussian processes.