Face Recognition Method Based on 2DLDA and SVM Optimated by PSO Algorithm

DOI

10.2991/mcei-16.2016.81How to use a DOI?

Keywords

Wavelet Transform; Two Dimensional Linear Discriminant Analysis; Particle Swarm Optimization; Support Vector Machine; Face Recognition

Abstract

Concerning the "Small Samples Size" problem in LDA algorithm and reduce the effects to the SVM face recognition rate caused by random parameters set by human. An algorithm based on combination with the PSO algorithm which was originated form artificial life and evolutionary computation to SVM's parameters election and optimization, and Wavelet Transform , two-dimensional LDA(2DLDA) was proposed. Firstly, the original images were decomposed into high-frequency and low-frequency Components by Wavelet Transform (WT). The high-frequency components were ignored, while the low-frequency components can be obtained. Then, the liner discriminant features were extracted by two-dimensional LDA (2DLDA). Finally, we use the PSO algorithm to SVM's parameters election and optimization. Experimental results based on ORL face database show the validity of the algorithm this paper proposed for face recognition and it can reach the recognition rate of 98%.

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/).

Download article (PDF)

TY  - CONF
AU  - Dan Zou
AU  - Hong Zhang
PY  - 2016/12
DA  - 2016/12
TI  - Face Recognition Method Based on 2DLDA and SVM Optimated by PSO Algorithm
BT  - Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 391
EP  - 399
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.81
DO  - 10.2991/mcei-16.2016.81
ID  - Zou2016/12
ER  -