3 papers
cs.CV2018
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…
cs.CV2018
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…
cs.CV2018
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…