Face ShapeNets for 3D Face Recognition

Abstract

In this paper, we present a deep learning-based method for 3D face recognition. Unlike some previous works, our process does not rely on face representation methods as a proxy step to be accepted by Convolutional Neural Networks (CNNs). Applying 2D CNNs to irregular domains such as 3D meshes is challenging. Therefore, we employed 3D ShapeNets to recognize faces covering the full 3D shape since 3D face datasets are available and 3D data augmentation techniques to enlarge 3D datasets are widespread. The reduced size of 3D datasets is overcome by an appropriate 3D data augmentation to train our model. 3D ShapeNets are commonly used to recognize and analyse objects. To the best of the authors’ knowledge, this is the first time they are used for face recognition. This research work focuses on the preprocessing step. Whatever the nature of the face image is (either 2D or 3D representation), and whatever the acquisition conditions are, a 3D regular mesh of each input face image is first generated. Furthermore, meshes are converted to voxels to get the occupancy grid across all possible views. Finally, 3D ShapeNets are trained and recognition tests are performed. Indeed, 3D ShapeNets prove the efficiency and efficacy of 3D shape analysis task in 3D face recognition. The experimental results show that 3D face recognition using deep 3D CNNs such as 3D ShapeNets leads to significant improvement over the state-of-the-art performance on LFPW, BU3DFE, and FRAV3D datasets, with competitive recognition rates of 94.25%, 97.9% and 98.31% respectively.

Keywords

• 3D data augmentation
• 3D face recognition
• 3D meshes
• 3D ShapeNets
• feature extraction
• voxelization