50 citations · 79 across the 3 of their papers we have counts for
5 papers
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…
Morphable Face Models - An Open Framework
Thomas Gerig, Andreas Morel-Forster, Clemens Blumer +4
In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of…
Gaussian Process Morphable Models
Marcel Lüthi, Christoph Jud, Thomas Gerig +1
Statistical shape models (SSMs) represent a class of shapes as a normal distribution of point variations, whose parameters are estimated from example shapes. Principal component an…