Palmprint Recognition Based on Modular PCA and LS-SVM

DOI

10.2991/iccmcee-15.2015.102How to use a DOI?

Keywords

Palmprint recognition; MPCA; LS-SVM classifier; single sample

Abstract

In this paper, in terms of feature extraction, according to the characteristics palmprint image we improve PCA algorithm and design modular PCA algorithms that is suitable for palmprint image. The whole palmprint image is divided into a plurality of sub-block images, then use principal component analysis.The improved feature extraction method is verified by the experiment and it can improve the recognition accuracy rate, especially in the case of single sample palmprint recognition. In the aspect of feature recognition, we train LS-SVM classifier to complete the palmprint recognition system.

Copyright

© 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/).

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TY  - CONF
AU  - Kunlun Li
AU  - Yaxin Zhang
AU  - HuanHuan Liu
AU  - Shuo Sun
PY  - 2015/11
DA  - 2015/11
TI  - Palmprint Recognition Based on Modular PCA and LS-SVM
BT  - Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 559
EP  - 564
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccmcee-15.2015.102
DO  - 10.2991/iccmcee-15.2015.102
ID  - Li2015/11
ER  -