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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| Vydáno v: | Proceedings (Brazilian Conference on Intelligent Systems. Online) s. 753 - 758 |
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| Hlavní autoři: | , , |
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| Jazyk: | angličtina |
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01.10.2019
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| ISSN: | 2643-6264 |
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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. |
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| 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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| 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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