Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition

Feng Liu, Ronghang Zhu, Dan Zeng, Qijun Zhao, Xiaoming Liu; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5216-5225

This paper proposes an encoder-decoder network to disentangle shape features during 3D face shape reconstruction from single 2D images, such that the tasks of learning discriminative shape features for face recognition and reconstructing accurate 3D face shapes can be done simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. We develop a multi image 3D morphable model (3DMM) fitting method for multiple 2D images of a subject to construct training data. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.