alternative to use the discarded portion of the spectra and extract real 3D information by projecting a high frequency light pattern. Hence, a low resolution version of the RGB image remains approximately invariant allowing the use of standard 2D approaches, while 3D information is extracted efficiently from the local deformation of the projected patterns.

![Real 3D face recognition is possible by capturing one single RGB image if a high frequency pattern is projected. The low frequency components of the captured image can be fed into a state-of-the-art 2D face recognition method, while the high frequency components encode local depth information that can be used to extract 3D facial features. It is important to highlight that, in contrast with most existing 3D alternatives, the proposed approach provides real 3D information, not 3D hallucination from the RGB input. As a result, state-of-the-art 2D face recognition methods can be enhanced with real 3D information.[]{data-label="fig:teaser"}](figures/teaser-3dface.pdf){width=".7\columnwidth"}

The proposed solution to extract 3D facial features has key differences with the two common approaches presented in existing literature: 3D hallucination [@eigen2014depth; @Huber2015; @Liu2015; @Pini2018] and 3D reconstruction [@Zafeiriou2013; @Zou2005]. We will discuss these differences in detail in the following section. We illustrate the main limitation of 3D 