A Probabilistic Approach To Realistic Face Synthesis With A Single Uncalibrated Image
Journal
IEEE Transaction on Image Processing
Date
2012.06.12
Abstract
This paper presents a novel approach to automatic
face modeling for realistic synthesis from an unknown face image,
using a probabilistic face diffuse model and a generic face
specular map. We construct a probabilistic face diffuse model
for estimating the albedo and the normals of the input face.
Then, we develop a generic face specular map for estimating
the specularity of the face. Using the estimated albedo, normal
and specular information, we can synthesize the face under
arbitrary lighting and viewing directions realistically. Unlike
many existing techniques, our approach can extract both the
diffuse and specular information of the face without involving
an intensive 3D matching procedure.
We conduct three different experiments to show our improvement
over the prior art. First, we compare the proposed
algorithm with previous techniques, including the state of the art,
to demonstrate our achievement in realistic face synthesis. Moreover,
we evaluate the proposed algorithm with non-automatic
face modeling techniques through a subjective study. This evaluation
is meaningful in that it tells us how far the proposed
algorithm as well as others are from the real photograph in
terms of the perceptual quality. Finally, we apply our face model
for improving the face recognition performance under varying
illumination conditions and show that the proposed algorithm
is effective to enhance the face recognition rate. Thanks to the
compact representation and the effective inference scheme, our
technique is applicable for many practical applications, such
as avatar creation, digital face cloning, face normalization, deidentification
and many others.