  Methods for generating synthetic data have become of increasing importance to
build large datasets required for Convolution Neural Networks (CNN) based deep
learning techniques for a wide range of computer vision applications. In this
work, we extend existing methodologies to show how 2D thermal facial data can
be mapped to provide 3D facial models. For the proposed research work we have
used tufts datasets for generating 3D varying face poses by using a single
frontal face pose. The system works by refining the existing image quality by
performing fusion based image preprocessing operations. The refined outputs
have better contrast adjustments, decreased noise level and higher exposedness
of the dark regions. It makes the facial landmarks and temperature patterns on
the human face more discernible and visible when compared to original raw data.
Different image quality metrics are used to compare the refined version of
images with original images. In the next phase of the proposed study, the
refined version of images is used to create 3D facial geometry structures by
using Convolution Neural Networks (CNN). The generated outputs are then
imported in blender software to finally extract the 3D thermal facial outputs
of both males and females. The same technique is also used on our thermal face
data acquired using prototype thermal camera (developed under Heliaus EU
project) in an indoor lab environment which is then used for generating
synthetic 3D face data along with varying yaw face angles and lastly facial
depth map is generated.
