KEYWORDS: 3D modeling, 3D image processing, Data modeling, Image processing, Principal component analysis, Databases, 3D image reconstruction, Reconstruction algorithms, Cameras, Light sources and illumination
3D face modeling has been one of the greatest challenges for researchers in computer graphics for many years. Various
methods have been used to model the shape and texture of faces under varying illumination and pose conditions from a
single given image. In this paper, we propose a novel method for the 3D face synthesis and reconstruction by using a
simple and efficient global optimizer. A 3D-2D matching algorithm which employs the integration of the 3D morphable
model (3DMM) and the differential evolution (DE) algorithm is addressed. In 3DMM, the estimation process of fitting
shape and texture information into 2D images is considered as the problem of searching for the global minimum in a
high dimensional feature space, in which optimization is apt to have local convergence. Unlike the traditional scheme
used in 3DMM, DE appears to be robust against stagnation in local minima and sensitiveness to initial values in face
reconstruction. Benefitting from DE's successful performance, 3D face models can be created based on a single 2D
image with respect to various illuminating and pose contexts. Preliminary results demonstrate that we are able to
automatically create a virtual 3D face from a single 2D image with high performance. The validation process shows that
there is only an insignificant difference between the input image and the 2D face image projected by the 3D model.
Pose and illumination are identified as major problems in 2D face recognition (FR). It has been theoretically proven that
the more diversified instances in the training phase, the more accurate and adaptable the FR system appears to be. Based
on this common awareness, researchers have developed a large number of photographic face databases to meet the
demand for data training purposes. In this paper, we propose a novel scheme for 2D face database diversification based
on 3D face modeling and computer graphics techniques, which supplies augmented variances of pose and illumination.
Based on the existing samples from identical individuals of the database, a synthesized 3D face model is employed to
create composited 2D scenarios with extra light and pose variations. The new model is based on a 3D Morphable Model
(3DMM) and genetic type of optimization algorithm. The experimental results show that the complemented instances
obviously increase diversification of the existing database.
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