A 3-D assisted generative model for facial texture super-resolution
Pouria Mortazavian, Josef Kittler and William Christmas
In: International Conference on Biometrics: Theory, Applications and Systems, Sept. 28-30, 2009.
Abstract—This paper describes an example-based Bayesian
method for 3D-assisted pose-independent facial texture superresolution.
The method utilizes a 3D morphable model to map
facial texture from a 2D face image to a pose- and shapenormalized
texture map and vice versa. The center piece of this
method is a generative model to describe the process of forming
an image from a pose- and shape-normalized texture map.
The goal is to reconstruct a high-resolution texture map given
an low-resolution face image. The prior knowledge about the
sought high-resolution texture is incorporated into the Bayesian
framework by using a recognition-based prior that encourages
the gradient values of the texture map to be close to some
We develop the generative model and formulate the problem
as MAP estimation. The results show that this framework is
capable of performing pose-independent face recognition even
when the sample set only contains exemplar face images with
frontal pose. We present results in frontal and non-frontal
poses. We also demonstrate that the technique can be utilized
to improve face recognition results when the probe images have
a lower resolution compared to the gallery images.