Face Recognition Based on Deep Autoencoder Networks with Dropout

DOI

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.

Copyright

© 2017, 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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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  - Proceedings of the 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  - 10.2991/msam-17.2017.54
ID  - Li2017/03
ER  -