A Quantitative Comparison of Methods for 3D Face Reconstruction from 2D Images

In the past years, many studies have highlighted the relation between deviations from normal facial morphology (dysmorphology) and some genetic and mental disorders. Recent advances in methods for reconstructing the 3D geometry of the face from 2D images opens new possibilities for dysmorphology research without the need for specialized 3D imaging equipment. However, it is unclear whether these methods could reconstruct the facial geometry with the required accuracy. In this paper we present a comparative study of some of the most relevant approaches for 3D face reconstruction from 2D images, including photometric-stereo, deep learning and 3D Morphable Model fitting. We address the comparison in qualitatively and quantitatively terms using a public database consisting of 2D images and 3D scans from 100 people. Interestingly, we find that some methods produce quite noisy reconstructions that do not seem realistic, whereas others look more natural. However, the latter do not seem to adequately capture the geometric variability that exists between different subjects and produce reconstructions that look always very similar across individuals, thus questioning their fidelity.