Durham, NC, USA\
    guillermo.sapiro@gmail.com\
bibliography:
- 'bibfile.bib'
title: |
    *Differential 3D Facial Recognition:*\
    Adding 3D to your State-of-the-Art 2D Method
---

Introduction {#sec:introduction}
============

Two-dimensional face recognition has become extremely popular as it can be ubiquitously deployed and large datasets are available. In the past several years, tremendous progress has been achieved in making 2D approaches more robust and useful in real-world applications. Though 2D face recognition has surpassed human performance in certain conditions, challenges remain to make it robust to facial poses, uncontrolled ambient illumination, aging, low-light conditions, and spoofing attacks [@Ding2016; @kemelmacher2016megaface; @Nech2017; @taigman2014deepface]. In the present work we address some of these issues by enhancing the captured RGB facial image with 3D information as illustrated in Figure \[fig:teaser\]. High resolution cameras became ubiquitous, although for 2D face recognition, we only need a facial image of moderate or low resolution. For example latest phones frontal camera have a very high resolution (e.g., $3088\times2320$ pixels) while the resolution of the input to most face recognition systems is limited to $224\times224$ pixels [@arcface; @parkhi2015deep; @schroff2015facenet; @taigman2014deepface; @Zulqarnain2018]. This means that, in the context of face recognition, we are drastically underutilizing most of the resolution of captured images. We propose an 