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Proceedings Paper

3D face recognition using depth filtering and deep convolutional neural network
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Paper Abstract

In this paper, we first estimate the accuracy of 3D facial surface reconstruction from real RGB-D depth maps using various depth filtering algorithms. Next, a new 3D face recognition algorithm using deep convolutional neural network is proposed. With the help of 3D face augmentation techniques different facial expressions from a single 3D face scan are synthesized and used for network learning. The performance of the proposed algorithm is compared in terms of 3D face recognition metrics and processing time with that of common 3D face recognition algorithms.

Paper Details

Date Published: 6 September 2019
PDF: 11 pages
Proc. SPIE 11137, Applications of Digital Image Processing XLII, 111371Y (6 September 2019); doi: 10.1117/12.2527541
Show Author Affiliations
Konstantin Dorofeev, Chelyabinsk State Univ. (Russian Federation)
Alexey Ruchay, Chelyabinsk State Univ. (Russian Federation)
Anastasia Kober, Federal Research Ctr. of Biological Systems and Agro-Technologies (Russian Federation)
Vitaly Kober, Chelyabinsk State Univ. (Russian Federation)
CICESE (Mexico)

Published in SPIE Proceedings Vol. 11137:
Applications of Digital Image Processing XLII
Andrew G. Tescher; Touradj Ebrahimi, Editor(s)

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