Neural Models for 3D Face Generation and Recognition

Abstract

This work introduces a new technique for 3D point clouds generation using a neural modeling system to handle the differences caused by heterogeneous depth cameras, and to generate a new face canonical compact representation. The proposed system reduces the stored 3D dataset size, and if required, provides an accurate dataset regeneration. Furthermore, the system generates neural models for all gallery point clouds and stores these models to represent the faces in the recognition or verification processes. For the probe cloud to be verified, a new model is generated specifically for that particular cloud and is matched against prestored gallery model presentations to identify the query cloud. This work also introduces the utilization of Siamese deep neural network in 3D face verification using generated model representations as raw data for the deep network, and shows that the accuracy of the trained network is comparable to all published results on Bosphorus dataset.