text: The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied "in the wild", their 3D estimates are either unstable and change for different photos of the same subject or they are over-regularized and generic. In response, we describe a robust method for regressing discriminative 3D morphable face models (3DMM). We use a convolutional neural network (CNN) to regress 3DMM shape and texture parameters directly from an input photo. We overcome the shortage of training data required for this purpose by offering a method for generating huge numbers of labeled examples. The 3D estimates produced by our CNN surpass state of the art accuracy on the MICC data set. Coupled with a 3D-3D face matching pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face shapes as representations, rather than the opaque deep feature vectors used by other modern systems.
  @cite_9: Human faces are remarkably similar in global properties, including size, aspect ratio, and location of main features, but can vary considerably in details across individuals, gender, race, or due to facial expression. We propose a novel method for 3D shape recovery of faces that exploits the similarity of faces. Our method obtains as input a single image and uses a mere single 3D reference model of a different person's face. Classical reconstruction methods from single images, i.e., shape-from-shading, require knowledge of the reflectance properties and lighting as well as depth values for boundary conditions. Recent methods circumvent these requirements by representing input faces as combinations (of hundreds) of stored 3D models. We propose instead to use the input image as a guide to "mold” a single reference model to reach a reconstruction of the sought 3D shape. Our method assumes Lambertian reflectance and uses harmonic representations of lighting. It has been tested on images taken under controlled viewing conditions as well as on uncontrolled images downloaded from the Internet, demonstrating its accuracy and robustness under a variety of imaging conditions and overcoming significant differences in shape between the input and reference individuals including differences in facial expressions, gender, and race.
  @cite_31: In this paper, we aim to recover the 3D shape of a human face using a single image. We use a combination of symmetric shape from shading by Zhao and Chellappa and statistical approach for facial shape reconstruction by Atick, Griffin and Redlich. Given a single frontal image of a human face under a known directional illumination from a side, we represent the solution as a linear combination of basis shapes and recover the coefficients using a symmetry constraint on a facial shape and albedo. By solving a single least-squares system of equations, our algorithm provides a closed-form solution which satisfies both symmetry and statistical constraints in the best possible way. Our procedure takes only a few seconds, accounts for varying facial albedo, and is simpler than the previous methods. In the special case of horizontal illuminant direction, our algorithm runs even as fast as matrix-vector multiplication.
summary: Scene assumption methods. In order to obtain correct reconstructions, some make strong assumptions on the scene and the viewing conditions in the input image. Shape from shading methods @cite_9 , for example, make assumptions on the light sources, facial reflectance and more. Others instead use facial symmetry @cite_31 . The assumptions they and others make often do not hold in practice, limiting the application of these methods to controlled settings.