Learning Hierarchical Representations for Face Recognition using Deep Belief Network Embedded with Softmax Regress and Multiple Neural Networks
- 10.2991/iwmecs-15.2015.1How to use a DOI?
- Face recognition, Semi-supervised, Hierarchical representations, Hybrid neural networks, RBM, Deep learning.
In face recognition and classi cation, feature extraction and classi cation based on insuf cient labeled data is a well-known challenging problem. In this paper, a novel semi-supervised learning algorithm named deep belief network embedded with Softmax regress (DBNESR) is proposed to address this problem. DBNESR first learns hierarchical representations of feature by deep learning and then makes more efficient classification with Softmax regress. At the same time we design many kinds of classifiers based on supervised learning: BP, HBPNNs, RBF, HRBFNNs, SVM and multiple classification decision fusion classifier——hybrid HBPNNs-HRBFNNs-SVM classifier. The conducted experiments validate: Firstly, the proposed semi-supervised deep learning algorithm DBNESR is optimal for face recognition with the highest and most stable recognition rates; Second, the semi-supervised learning algorithm has better effect than all supervised learning algorithms; Third, hybrid neural networks has better effect than single neural network.
- © 2015, 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 - Haijun Zhang AU - Nanfeng Xiao PY - 2015/10 DA - 2015/10 TI - Learning Hierarchical Representations for Face Recognition using Deep Belief Network Embedded with Softmax Regress and Multiple Neural Networks BT - Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences PB - Atlantis Press SP - 1 EP - 7 SN - 2352-538X UR - https://doi.org/10.2991/iwmecs-15.2015.1 DO - 10.2991/iwmecs-15.2015.1 ID - Zhang2015/10 ER -