Abstract:
3D facial reconstruction is an emerging and interesting application in the field of
computer graphics and computer vision. It is difficult and challenging to reconstruct the 3D
facial model from a single photo because of arbitrary poses, non-uniform illumination,
expressions, and occlusions. 3D face reconstruction algorithms used for application such as
3D printing, 3D VR games and facial recognition. Mostly algorithms use convolution
neural networks and deep learning frameworks to create facial model. Detailed 3D facial
models are difficult to reconstruct because every algorithm has some limitations related to
profile view, fine detail, accuracy, and speed. The major problem is to develop 3D face
with texture of large poses, wild faces, large training data, and occluded faces across facial
beard with single input image. I have proposed a solution which is used to reconstruct 3D
facial reconstruction across facial beard with different expressions, poses and illumination
by using convolution neural networks. In this work facial beard datasets have been
developed with their respective landmarks and 3DMM fitting. The algorithm is proposed
for generating the 3D facial beard model by using the single input image. The experimental
analysis is done quantitative and qualitative on the developed facial beard dataset. The 3D
face alignment across facial beard poses is performed by using the proposed approach.
