Reference : Disentangled Face Identity Representations for joint 3D Face Recognition and Expressi...
E-prints/Working papers : Already available on another site
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/48518
Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation
English
Kacem, Anis mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
cherenkova, kseniya [Artec3D]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
20-Apr-2021
No
[en] 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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Computer Vision Imaging & Machine Intelligence (CVI┬▓)
http://hdl.handle.net/10993/48518
https://arxiv.org/abs/2104.10273
FnR ; FNR11643091 > Djamila Aouada > IDform > Face Identification Under Deformations > 01/05/2018 > 31/10/2021 > 2017

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