Face Reconstruction Algorithm based on Lightweight Convolutional Neural Networks and Channel-wise Attention

Abstract

3D face reconstruction and face alignment are two highly relevant topics in face research. However, for these tasks, computational complexity is another consideration besides the training accuracy of the model. Our goal is to regress the 3D facial geometry and dense correspondence information from the given 2D image. Thus, in this paper, we fit the 3D morphable model based on a lightweight convolution neural network of the ShuffleNetV2 Plus series network and channel-wise attention model, which can improve the representation ability of the network and the performance of the 3D face reconstruction task without increasing the number of network parameters. Evaluations on test datasets show that our approach achieves significant performance improvements on both 3D face reconstruction and dense face alignment tasks. Alignment performances evaluate on AFLW2000-3D, and our method obtains a lower mean Normalized Mean Error (NME(%)) of 3.694.