Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

Face Recognition Based on Deep Autoencoder Networks with Dropout

Authors
Fang Li, Xiang Gao, Liping Wang
Corresponding Author
Fang Li
Available Online March 2017.
DOI
https://doi.org/10.2991/msam-17.2017.54How to use a DOI?
Keywords
deep- autoencoder networks; dropout; face recognition
Abstract
Though deep autoencoder networks show excellent ability in learning feature, its poor performance on test data go against visualization and classification of image. In particular, a standard neural net with multi-hidden layers typically fails to work when sample size is small. In order to improve the generalization ability and reduce over-fitting, we apply dropout to optimize the deep autoencoder networks. In this paper, we propose face recognition based on deep autoencoder networks with dropout. Our experiments show that deep autoencoder networks with dropout yield significantly lower test error, and bring a new conception in pattern recognition with deep learning.
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Proceedings
2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
978-94-6252-324-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-17.2017.54How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Fang Li
AU  - Xiang Gao
AU  - Liping Wang
PY  - 2017/03
DA  - 2017/03
TI  - Face Recognition Based on Deep Autoencoder Networks with Dropout
BT  - 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
PB  - Atlantis Press
SP  - 243
EP  - 246
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-17.2017.54
DO  - https://doi.org/10.2991/msam-17.2017.54
ID  - Li2017/03
ER  -