Expression Removal in 3D Faces for Recognition Purposes

We present an encoder-decoder neural network to remove deformations caused by expressions from 3D face images. It receives a 3D face with or without expressions as input and outputs its neutral form. Our objective is not to obtain the most realistic results but to enhance the accuracy of 3D face rec...

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Published in:Proceedings (Brazilian Conference on Intelligent Systems. Online) pp. 753 - 758
Main Authors: Amparo Barbosa, Lucas, Dahia, Gabriel, Pamplona Segundo, Mauricio
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
3D Images
Deep Learning
Face recognition
Facial Recognition
Image recognition
Neural networks
Strain
Three-dimensional displays
Training
Two dimensional displays
ISSN:2643-6264
Online Access:Get full text
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Abstract We present an encoder-decoder neural network to remove deformations caused by expressions from 3D face images. It receives a 3D face with or without expressions as input and outputs its neutral form. Our objective is not to obtain the most realistic results but to enhance the accuracy of 3D face recognition systems. To this end, we propose using a recognition-based loss function during training so that our network can learn to maintain important identity cues in the output. Our experiments using the Bosphorus 3D Face Database show that our approach successfully reduces the difference between face images from the same subject affected by different expressions and increases the gap between intraclass and interclass difference values. They also show that our synthetic neutral images improved the results of four different well-known face recognition methods.
AbstractList We present an encoder-decoder neural network to remove deformations caused by expressions from 3D face images. It receives a 3D face with or without expressions as input and outputs its neutral form. Our objective is not to obtain the most realistic results but to enhance the accuracy of 3D face recognition systems. To this end, we propose using a recognition-based loss function during training so that our network can learn to maintain important identity cues in the output. Our experiments using the Bosphorus 3D Face Database show that our approach successfully reduces the difference between face images from the same subject affected by different expressions and increases the gap between intraclass and interclass difference values. They also show that our synthetic neutral images improved the results of four different well-known face recognition methods.
Author Pamplona Segundo, Mauricio
Dahia, Gabriel
Amparo Barbosa, Lucas
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  givenname: Mauricio
  surname: Pamplona Segundo
  fullname: Pamplona Segundo, Mauricio
  organization: Federal University of Bahia, Brazil
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Snippet We present an encoder-decoder neural network to remove deformations caused by expressions from 3D face images. It receives a 3D face with or without...
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StartPage 753
SubjectTerms 3D Images
Deep Learning
Face recognition
Facial Recognition
Image recognition
Neural networks
Strain
Three-dimensional displays
Training
Two dimensional displays
Title Expression Removal in 3D Faces for Recognition Purposes
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