326 citations · 334 across the 4 of their papers we have counts for
9 papers
Face Morphing: Fooling a Face Recognition System Is Simple!
Stefan Hörmann, Tianlin Kong, Torben Teepe +3
State-of-the-art face recognition (FR) approaches have shown remarkable results in predicting whether two faces belong to the same identity, yielding accuracies between 92% and 100…
Towards a Deeper Understanding of Skeleton-based Gait Recognition
Torben Teepe, Johannes Gilg, Fabian Herzog +2
Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns. In recent years, most gait recognition…
Attention-based Partial Face Recognition
Stefan Hörmann, Zeyuan Zhang, Martin Knoche +2
Photos of faces captured in unconstrained environments, such as large crowds, still constitute challenges for current face recognition approaches as often faces are occluded by obj…
GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
Torben Teepe, Ali Khan, Johannes Gilg +3
Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette ima…
A Multi-Task Comparator Framework for Kinship Verification
Stefan Hörmann, Martin Knoche, Gerhard Rigoll
Approaches for kinship verification often rely on cosine distances between face identification features. However, due to gender bias inherent in these features, it is hard to relia…
DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks
Nico Engel, Stefan Hoermann, Markus Horn +2
We address the problem of vehicle self-localization from multi-modal sensor information and a reference map. The map is generated off-line by extracting landmarks from the vehicle'…
Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks
Marcel Schreiber, Stefan Hoermann, Klaus Dietmayer
We tackle the long-term prediction of scene evolution in a complex downtown scenario for automated driving based on Lidar grid fusion and recurrent neural networks (RNNs). A bird's…
Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs
Nico Engel, Stefan Hoermann, Philipp Henzler +1
The comprehensive representation and understanding of the driving environment is crucial to improve the safety and reliability of autonomous vehicles. In this paper, we present a n…
Offline Object Extraction from Dynamic Occupancy Grid Map Sequences
Daniel Stumper, Fabian Gies, Stefan Hoermann +1
A dynamic occupancy grid map (DOGMa) allows a fast, robust, and complete environment representation for automated vehicles. Dynamic objects in a DOGMa, however, are commonly repres…