  We present a method for highly efficient landmark detection that combines
deep convolutional neural networks with well established model-based fitting
algorithms. Motivated by established model-based fitting methods such as active
shapes, we use a PCA of the landmark positions to allow generative modeling of
facial landmarks. Instead of computing the model parameters using iterative
optimization, the PCA is included in a deep neural network using a novel layer
type. The network predicts model parameters in a single forward pass, thereby
allowing facial landmark detection at several hundreds of frames per second.
Our architecture allows direct end-to-end training of a model-based landmark
detection method and shows that deep neural networks can be used to reliably
predict model parameters directly without the need for an iterative
optimization. The method is evaluated on different datasets for facial landmark
detection and medical image segmentation. PyTorch code is freely available at
https://github.com/justusschock/shapenet
