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

• URL: http://arxiv.org/abs/2104.10273v1
• Date: Tue, 20 Apr 2021 22:33:10 GMT
• Title: Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation
• Authors: Anis Kacem, Kseniya Cherenkova, Djamila Aouada
• Abstract summary: 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 into those of neutral faces, (3) and an identity recognition sub-network taking advantage of the neutralized latent representations for 3D face recognition.
• Score: 20.854071758664297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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