50 citations · 68 across the 6 of their papers we have counts for
7 papers · 1 filter
GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface Registration Using Gaussian Process Regression
Dennis Madsen, Jonathan Aellen, Andreas Morel-Forster +2
In this paper, we unify popular non-rigid registration methods for point sets and surfaces under our general framework, GiNGR. GiNGR builds upon Gaussian Process Morphable Models (…
Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation
Chunlu Li, Andreas Morel-Forster, Thomas Vetter +2
In this work, we aim to enhance model-based face reconstruction by avoiding fitting the model to outliers, i.e. regions that cannot be well-expressed by the model such as occluders…
A Closest Point Proposal for MCMC-based Probabilistic Surface Registration
Dennis Madsen, Andreas Morel-Forster, Patrick Kahr +3
We propose to view non-rigid surface registration as a probabilistic inference problem. Given a target surface, we estimate the posterior distribution of surface registrations. We…
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Adam Kortylewski, Mario Wieser, Andreas Morel-Forster +4
Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, tex…
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?
Adam Kortylewski, Bernhard Egger, Andreas Morel-Forster +5
It is well known that deep learning approaches to face recognition and facial landmark detection suffer from biases in modern training datasets. In this work, we propose to use syn…
Training Deep Face Recognition Systems with Synthetic Data
Adam Kortylewski, Andreas Schneider, Thomas Gerig +3
Recent advances in deep learning have significantly increased the performance of face recognition systems. The performance and reliability of these models depend heavily on the amo…
Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems
Adam Kortylewski, Bernhard Egger, Andreas Schneider +3
It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only…