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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Vydané v:Proceedings (Brazilian Conference on Intelligent Systems. Online) s. 753 - 758
Hlavní autori: Amparo Barbosa, Lucas, Dahia, Gabriel, Pamplona Segundo, Mauricio
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2019
Predmet:
3D Images
Deep Learning
Face recognition
Facial Recognition
Image recognition
Neural networks
Strain
Three-dimensional displays
Training
Two dimensional displays
ISSN:2643-6264
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Popis
Shrnutí: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.
ISSN:2643-6264
DOI:10.1109/BRACIS.2019.00135