  Various factors, such as identities, views (poses), and illuminations, are
coupled in face images. Disentangling the identity and view representations is
a major challenge in face recognition. Existing face recognition systems either
use handcrafted features or learn features discriminatively to improve
recognition accuracy. This is different from the behavior of human brain.
Intriguingly, even without accessing 3D data, human not only can recognize face
identity, but can also imagine face images of a person under different
viewpoints given a single 2D image, making face perception in the brain robust
to view changes. In this sense, human brain has learned and encoded 3D face
models from 2D images. To take into account this instinct, this paper proposes
a novel deep neural net, named multi-view perceptron (MVP), which can untangle
the identity and view features, and infer a full spectrum of multi-view images
in the meanwhile, given a single 2D face image. The identity features of MVP
achieve superior performance on the MultiPIE dataset. MVP is also capable to
interpolate and predict images under viewpoints that are unobserved in the
training data.
