Face Recognition Based on Deep Autoencoder Networks with Dropout
Fang Li, Xiang Gao, Liping Wang
Available Online March 2017.
- 10.2991/msam-17.2017.54How to use a DOI?
- deep- autoencoder networks; dropout; face recognition
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.
- © 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/).
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 - 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 -