a (standard) texture image and depth gradient information, we can proceed to extract 2D and 3D facial features from each component. To this end, state-of-the-art network architectures are evaluated. Our method is agnostic to the RGB and depth feature extractors, moreover, as the retrieved texture image is close to a standard RGB facial images (in sense of the L2-norm), any pre-train 2D feature extractor can be used (e.g., [@arcface; @parkhi2015deep; @schroff2015facenet; @taigman2014deepface; @Zulqarnain2018]). In the experiments presented in this section we tested a network based on the Xception architecture [@chollet2017xception] (details are provided as supplementary material). For the extraction of texture features, the network is trained using FaceScrub [@FaceScrub] dataset (as we previously described, this is a public dataset of 2D facial images). The module that extracts 3D facial features is trained using $2/3$ of the subjects of ND-2006 dataset, leaving the remaining subjects exclusively for testing. The output of each module is a 512-dimensional feature vector (see, e.g., Figure \[fig:proposedFramework\]), hence the concatenation of 2D+3D features leads to a 1024-dimensional feature vector. Figure \[fig:embedding\] illustrates a 2D embedding of the texture features, the depth features, and the combination of both. The 2D mapping is learned by optimizing the t-SNE 