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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Veröffentlicht in:	Proceedings (Brazilian Conference on Intelligent Systems. Online) S. 753 - 758
Hauptverfasser:	Amparo Barbosa, Lucas, Dahia, Gabriel, Pamplona Segundo, Mauricio
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2019
Schlagworte:	3D Images Deep Learning Face recognition Facial Recognition Image recognition Neural networks Strain Three-dimensional displays Training Two dimensional displays
ISSN:	2643-6264
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	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