1 code implementation • 19 Nov 2018 • Adam Kortylewski, Bernhard Egger, Andreas Morel-Forster, Andreas Schneider, Thomas Gerig, Clemens Blumer, Corius Reyneke, Thomas Vetter
We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data.
no code implementations • CVPR 2018 • Dennis Madsen, Marcel Lüthi, Andreas Schneider, Thomas Vetter
We create a probabilistic joint face-skull model and show how to obtain a distribution of plausible face shapes given a skull shape.
2 code implementations • 16 Feb 2018 • Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster, Thomas Vetter
In our experiments with an off-the-shelf face recognition software we observe the following phenomena: 1) The amount of real training data needed to train competitive deep face recognition systems can be reduced significantly.
2 code implementations • 5 Dec 2017 • Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, Thomas Vetter
4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation.
no code implementations • ICCV 2017 • Andreas Schneider, Sandro Schonborn, Lavrenti Frobeen, Bernhard Egger, Thomas Vetter
Therefore, we propose to learn self-shadowing for Morphable Model parameters directly with a linear model.