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A face reconstruction method based on fusion regression network and gradient descent
Author(s): Menglin Zhao; Ming Yan
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Paper Abstract

Three-dimensional (3D) face reconstruction refers to the restoration and reconstruction of 3D model of face from one or more two-dimensional (2D) images. It has been widely used in face recognition, expression migration, face editing and other aspects. In the current existing algorithms, there are still many shortcomings in how to reconstruct 3D face by parametric model in real time. In this paper, based on the convolutional neural network, we integrate the weight mask into the loss function, and then use the back propagation algorithm to calculate the parameter gradient error. Finally, the parameter self-renewal purpose of the loss function is achieved by gradient descent. It can be seen from the experimental results that this method can accurately reconstruct the 3D contour of the face, and the reconstruction results are complete and the topological structure is known. This is very important for the application after face reconstruction, such as face changing, expression changing and other aspects of accuracy has been greatly improved.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132121 (27 November 2019); doi: 10.1117/12.2542617
Show Author Affiliations
Menglin Zhao, Communication Univ. of China (China)
Ming Yan, Communication Univ. of China (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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