in particular, the success of deep neural networks, reshaped the field and yielded more efficient, accurate, and reliable 2D methods such as: ArcFace[@arcface], VGG-Face [@parkhi2015deep], DeepFace [@taigman2014deepface], and FaceNet [@schroff2015facenet]. In spite of this, spoofing attacks and variations in pose, expression and illumination are still active challenges and significant efforts are being made to address them [@Cao2018; @Hayat2017; @He2018; @Kumar2018; @Lezama2017; @Liu2018; @Tran2017; @Yu2017; @Zhao2018; @Zou2005]. For example, Deng et al. [@Deng2018] attempt to handle large pose discrepancy between samples. To that end, they propose an adversarial facial UV map completion GAN. Complementing previous approaches that seek for robust feature representations, several works propose more robust loss and metric functions [@Liu2017; @Wang2018].

#### 3D hallucination from single RGB.

To enhance 2D approaches a common trend is to hallucinate a 3D representation from an input RGB image which is used to extract 3D features [@blanz2003face; @Dou2017; @eigen2014depth; @Huber2015; @Liu2015; @Pini2018]. For example, Cui et al. [@Cui2018] introduce a cascade of networks that simultaneously recover depth from an RGB input while seeking for separability of individual subjects. The estimated depth information is then used as a complementary modality to RGB.

#### 3D face recognition.

The approaches described previously share an important practical 