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In this paper, we propose a new deep learning-based approach fordisentangling face identity representations from expressive 3D faces. Given a3D face, our approach not only extracts a disentangled identity representationbut also generates a realistic 3D face with a neutral expression whilepredicting its identity. The proposed network consists of three components; (1)a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latentrepresentations, (2) a Generative Adversarial Network (GAN) that translates thelatent representations of expressive faces into those of neutral faces, (3) andan identity recognition sub-network taking advantage of the neutralized latentrepresentations for 3D face recognition. The whole network is trained in anend-to-end manner. Experiments are conducted on three publicly availabledatasets showing the effectiveness of the proposed approach.

Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation