Proceedings of the 2016 International Conference on Mechanics, Materials and Structural Engineering

Facial Feature Point Location in The Neural Network With Few Training Samples

Authors
Zhengyong Chen, Xiaohang Zhang, Jiaqi Shi, Shuang Zheng, Xiaodan Zou
Corresponding Author
Zhengyong Chen
Available Online March 2016.
DOI
10.2991/icmmse-16.2016.64How to use a DOI?
Keywords
Facial feature point location, Small sample training, Neural network, Sub-network
Abstract

This paper investigates a method to locate important facial feature points with small training samples. Firstly, the facial feature points are divided into several categories, then these various feature points are trained by LMBP neural network to get each sub-network. Outputs of these sub-networks can be combined to locate the important facial feature points. The experimental results show this kind of method based on neural network performs well, especially its calculation speed is fast, it can be applied in analysis of the facial expressions, facial reconstruction and other aspects.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Mechanics, Materials and Structural Engineering
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-187-2
ISSN
2352-5401
DOI
10.2991/icmmse-16.2016.64How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhengyong Chen
AU  - Xiaohang Zhang
AU  - Jiaqi Shi
AU  - Shuang Zheng
AU  - Xiaodan Zou
PY  - 2016/03
DA  - 2016/03
TI  - Facial Feature Point Location in The Neural Network With Few Training Samples
BT  - Proceedings of the 2016 International Conference on Mechanics, Materials and Structural Engineering
PB  - Atlantis Press
SP  - 385
EP  - 390
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmse-16.2016.64
DO  - 10.2991/icmmse-16.2016.64
ID  - Chen2016/03
ER  -