hallucination in the context of face recognition in Figure \[fig:3dillustration\], which emphasizes the lack of real 3D information on a standard RGB input image. We demonstrate that it is possible to extract actual 3D facial features bypassing the ill-posed problem of explicit depth estimation. Our contributions are summarized as follows:

-   Analyzing the spectral content of thousands of facial images, we design a high frequency light pattern that simultaneously allow us to retrieve a standard 2D low resolution facial image plus a 3D gradient facial representation.

-   We propose an effective and modular solution that achieves 2D and 3D information decomposition and facial feature extraction in a data-driven fashion (bypassing a 3D facial reconstruction).

-   We show that by defining an adequate distance function in the space of the feature embedding, we can leverage the advantages of both 2D and 3D features. We can transparently exploit existing state-of-the-art 2D methods and improve their robustness, e.g., to spoofing attacks.

Related Work {#sec:relatedWork}
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To recognize or validate the identity of a subject from a 2D color photograph is a longstanding problem of computer vision and has been largely studied for over forty years [@kaya1972basic; @zhao2003face]. Recent advances in machine learning, and 