architectures and the training protocols are provided as supplementary material). Using ground truth texture and depth facial information, we simulated the projection of the designed pattern over the $888$ subjects provided in ND-2006 dataset. Illustrative results are presented in Figure \[fig:nd2006patterns\] and in the supplementary material. The 3D geometrical model and a detailed description of the simulation process is provided in Section \[app:sec:sim\_proj\_pat\]. Though the simulation of the deformation of a projected pattern can be computed in a relatively simple manner (if the depth information is known), the inverse problem is analytically hard [@DiMartino2015one; @Rosman2016; @zhang2013handbook].

![Active light projection. From left to right: ground truth RGB facial image, 3D facial scanner, and finally the image we would acquire if the designed high frequency pattern is projected over the face. Two random samples from ND-2006 are illustrated.[]{data-label="fig:nd2006patterns"}](figures/nd2006patterns.png){width=".6\columnwidth"}

Despite the previous, we observed that a stack of convolutional layers can efficiently learn how to infer from the image with the projected pattern, both depth gradient information, and the standard (2D) facial image. Figure \[fig:texture\_descomposition\] illustrates some results for subjects in the test set. The first column corresponds to the input to the network, the second column the ground truth texture information, and 