A face reconstruction method based on fusion regression network and gradient descent

摘要

Three-dimensional (3D) face reconstruction refers to the restoration and reconstruction of 3D model of face from one or more two-dimensional (2D) images. It has been widely used in face recognition, expression migration, face editing and other aspects. In the current existing algorithms, there are still many shortcomings in how to reconstruct 3D face by parametric model in real time. In this paper, based on the convolutional neural network, we integrate the weight mask into the loss function, and then use the back propagation algorithm to calculate the parameter gradient error. Finally, the parameter self-renewal purpose of the loss function is achieved by gradient descent. It can be seen from the experimental results that this method can accurately reconstruct the 3D contour of the face, and the reconstruction results are complete and the topological structure is known. This is very important for the application after face reconstruction, such as face changing, expression changing and other aspects of accuracy has been greatly improved.