Research and Application on Face Recognition Algorithm Based on FWLD Method and Deep Belief Nets

DOI

10.2991/iccia-17.2017.96How to use a DOI?

Keywords

Weber local descriptor; deep belief nets; fuzzy logic; local binary pattern.

Abstract

Aiming at the problem of insufficient feature extraction and sensitive to noise for traditional face recognition algorithms, a face recognition algorithm based on improved Weber local descriptor and deep belief nets is proposed. First analyze the shortcomings of Weber local descriptor, and based on the fuzzy logic, an improved FWLD face description method is proposed. To make full use of the domain pixels in WLD direction component, based on introduction of LBP coding, the fuzzy logic is used to optimize the LBP coding, and then the improved WLD algorithm is used as the input of the deep belief network. And the network parameters are obtained through the layer-by-layer greedy pre-training network. Finally, the BP neural network is used to fine tune and optimize the DBN network, and the test samples are predicted by the trained network. In the ORL data set, the correct rate is 95%. The simulation results show that the face recognition algorithm proposed in this paper is higher in recognition rate and more robust than the traditional recognition.

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  - Yihong Zhang
AU  - Xiang Li
AU  - Zhijie Wang
PY  - 2016/07
DA  - 2016/07
TI  - Research and Application on Face Recognition Algorithm Based on FWLD Method and Deep Belief Nets
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 570
EP  - 575
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.96
DO  - 10.2991/iccia-17.2017.96
ID  - Zhang2016/07
ER  -