  Can faces acquired by low-cost depth sensors be useful to catch some
characteristic details of the face? Typically the answer is no. However, new
deep architectures can generate RGB images from data acquired in a different
modality, such as depth data. In this paper, we propose a new
\textit{Deterministic Conditional GAN}, trained on annotated RGB-D face
datasets, effective for a face-to-face translation from depth to RGB. Although
the network cannot reconstruct the exact somatic features for unknown
individual faces, it is capable to reconstruct plausible faces; their
appearance is accurate enough to be used in many pattern recognition tasks. In
fact, we test the network capability to hallucinate with some
\textit{Perceptual Probes}, as for instance face aspect classification or
landmark detection. Depth face can be used in spite of the correspondent RGB
images, that often are not available due to difficult luminance conditions.
Experimental results are very promising and are as far as better than
previously proposed approaches: this domain translation can constitute a new
way to exploit depth data in new future applications.
