Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

Face Recognition Based on LBP(2D)2PCA+SVM

Authors
Yanmei Jiang, Wei Wei, Xue Liu, Ying Shi
Corresponding Author
Yanmei Jiang
Available Online February 2018.
DOI
https://doi.org/10.2991/csece-18.2018.37How to use a DOI?
Keywords
face recognition; (2D)2PCA; LBP; SVM
Abstract
In this work, we present a novel approach of face recognition which considers local binary pattern (LBP), principal component analysis (PCA) and support vector machine (SVM) algorithms, which is named as LBP(2D)2PCA+SVM. This method firstly extracts facial texture features, then uses (2D)2PCA algorithm to reduce its dimensions, and finally employs SVM algorithm to classify and recognize the images. Our consideration is that LBP algorithm has a characteristic of rotation invariance, which is robust to illumination change and pose variation; (2D)2PCA is an improvement of PCA, using this method the image can reach the maximum degree of dimension reduction; SVM has the advantages of global optimization, simple structure, strong generalization ability and so on. By synthesizing the advantages of the algorithms, our proposed LBP(2D)2PCA +SVM algorithm performs higher recognition rate than other algorithms, and the effective combination of different strategies can greatly help us to improve the recognition rate.
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Proceedings
2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
Part of series
Advances in Computer Science Research
Publication Date
February 2018
ISBN
978-94-6252-487-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/csece-18.2018.37How 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  - Yanmei Jiang
AU  - Wei Wei
AU  - Xue Liu
AU  - Ying Shi
PY  - 2018/02
DA  - 2018/02
TI  - Face Recognition Based on LBP(2D)2PCA+SVM
BT  - 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.37
DO  - https://doi.org/10.2991/csece-18.2018.37
ID  - Jiang2018/02
ER  -