Face Recognition based on Modular 2DPCA and Contextual Constraints based Kernel Discriminant Analysis

DOI

10.2991/iccsee.2013.461How to use a DOI?

Keywords

M2DPCA, CCLDA,CCKDA, face recognition

Abstract

In this paper, an improved face recognition algorithm is proposed based on the combination of modular 2DPCA and contextual constraints based kernel discriminant analysis (CCKDA) because of the disadvantages of CCLDA. CCLDA first transforms an image matrix to a vector which caused high dimensionality and computational complexity and not considers the local feature. While our method first extracts the local features with the original images which are divided into modular sub-images, then CSKDA is utilized, which incorporates the contextual information into kernel discriminant analysis. Experimental results obtained on ORL and XM2VTS databases show the effectiveness of the new method.

Copyright

© 2013, 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  - Hua-Li Feng
PY  - 2013/03
DA  - 2013/03
TI  - Face Recognition based on Modular 2DPCA and Contextual Constraints based Kernel Discriminant Analysis
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1838
EP  - 1841
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.461
DO  - 10.2991/iccsee.2013.461
ID  - Feng2013/03
ER  -