fringes and the system baseline can be optimized if faces present a narrower spectral content in a particular direction. We study the texture and depth spectrum of the facial images of ND-2006 dataset (this dataset provides ground truth facial texture and depth information). We observed (see Figure \[fig:face\_spectrum\]) that for facial images sampled at a $480\times480$ spatial resolution, most of the energy is concentrated in a third of the discrete spectral domain (observe the extracted one dimensional profiles of the spectrum shown at the left side of Figure \[fig:face\_spectrum\]). In addition, we observe that the spectral content of facial images is approximately isotropic. See, for example, Figure \[fig:face\_spectrum\] and observe how for 1-dimensional sections across different orientations the 2D spectra envelope is almost constant. We conclude that the orientation of the fringes does not play a significant role in the context of facial analysis. In addition, we conclude that the fringes width should be smaller than $7$mm (distance measure over the face).[^3]

Network training and the advantages of modularity. {#sec:networkDesign}
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As described previously, the parameters of the proposed solution can be split in three groups $\theta = (\theta_g, \theta_{rgb}, \theta_{\nabla z})$. This is an important practical property and we 