Voxelized Facial Reconstruction Using Deep Neural Network

Xiaoshuang Li, Bin Sheng, Ping Li, Jinman Kim, David Dagan Feng

Abstract: This paper presents an approach to predicting variation tendency of human faces with regard to cranium changes based on deep learning. Our work focuses on generating individual customized facial models with high plausibility. Inspired by the performance of encoder-decoder convolutional neural network, the core trainable predicting engine of our learning network is designed for three-dimension voxelized data representation as the encoder-decoder structure and the encoder part is similar to the 7 layers of VGG16 network. To take full consideration of the cranium changes and features of original human face, a novel formation of channeled volumetric data structure is presented, and also the corresponding sub and up-sampling strategies for volume data. Our encoder-decoder neural network consumes discrete 3-channel volume data and generates 1-channel volume data as predicted post-variation human face. This framework is quantified with clinical dataset and it shows that its' performance improves in comparison with the state-of-the-art technologies.