[@nd2006]. FaceScrub contains $100k$ RGB (2D) facial images of $530$ different subjects, and is used to train the texture-based facial embedding. CASIA dataset contains $150$ genuine videos (recording a person) and $450$ videos of different types of spoofing attacks, the data was collected for $50$ subjects. We use this dataset to simulate and imitate the texture properties of images of spoofing attacks. ND-2006 is one of the larges publicly available datasets with 2D and 3D facial information, it contains $13k$ images of $888$ subjects. We used this set to demonstrate that differential 3D features can be extracted from a single RGB input, to compare RGB features with 3D features extracted from the differential 3D input, and to show that when 2D and 3D information is properly combined, the best properties of each can be obtained.

#### Texture and differential 3D decomposition.

In Section \[sec:pattern design\] we discussed how real 3D information and texture information can be coded and later extracted using a single RGB image. In addition, we argue that this decomposition can be learned efficiently and effectively in a data-driven fashion. To that end, we tested simple network architectures composed of standard convolutional layers (a full description of these 