1 research outputs found
GmFace: A Mathematical Model for Face Image Representation Using Multi-Gaussian
Establishing mathematical models is a ubiquitous and effective method to
understand the objective world. Due to complex physiological structures and
dynamic behaviors, mathematical representation of the human face is an
especially challenging task. A mathematical model for face image representation
called GmFace is proposed in the form of a multi-Gaussian function in this
paper. The model utilizes the advantages of two-dimensional Gaussian function
which provides a symmetric bell surface with a shape that can be controlled by
parameters. The GmNet is then designed using Gaussian functions as neurons,
with parameters that correspond to each of the parameters of GmFace in order to
transform the problem of GmFace parameter solving into a network optimization
problem of GmNet. The face modeling process can be described by the following
steps: (1) GmNet initialization; (2) feeding GmNet with face image(s); (3)
training GmNet until convergence; (4) drawing out the parameters of GmNet (as
the same as GmFace); (5) recording the face model GmFace. Furthermore, using
GmFace, several face image transformation operations can be realized
mathematically through simple parameter computation.Comment: 12 pages, 12 figures, 4 table