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Can faces acquired by low-cost depth sensors be useful to catch somecharacteristic details of the face? Typically the answer is no. However, newdeep architectures can generate RGB images from data acquired in a differentmodality, such as depth data. In this paper, we propose a new\textit{Deterministic Conditional GAN}, trained on annotated RGB-D facedatasets, effective for a face-to-face translation from depth to RGB. Althoughthe network cannot reconstruct the exact somatic features for unknownindividual faces, it is capable to reconstruct plausible faces; theirappearance is accurate enough to be used in many pattern recognition tasks. Infact, we test the network capability to hallucinate with some\textit{Perceptual Probes}, as for instance face aspect classification orlandmark detection. Depth face can be used in spite of the correspondent RGBimages, that often are not available due to difficult luminance conditions.Experimental results are very promising and are as far as better thanpreviously proposed approaches: this domain translation can constitute a newway to exploit depth data in new future applications.

Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face