  In this paper, we propose a new deep learning-based approach for
disentangling face identity representations from expressive 3D faces. Given a
3D face, our approach not only extracts a disentangled identity representation
but also generates a realistic 3D face with a neutral expression while
predicting its identity. The proposed network consists of three components; (1)
a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent
representations, (2) a Generative Adversarial Network (GAN) that translates the
latent representations of expressive faces into those of neutral faces, (3) and
an identity recognition sub-network taking advantage of the neutralized latent
representations for 3D face recognition. The whole network is trained in an
end-to-end manner. Experiments are conducted on three publicly available
datasets showing the effectiveness of the proposed approach.
