Fuzzy Kernel Two-dimensional Principal Component Analysis for Face Recognition

DOI

10.2991/aiie-15.2015.99How to use a DOI?

Keywords

face recognition; kernel two-dimensional principal component analysis (K2DPCA); fuzzy; class separability criterion

Abstract

The traditional kernel two-dimensional principal component analysis (K2DPCA) method did not take full advantage of the class information for face images and there are both “outer class” problem and “hard classifier” problem on face recognition. Therefore, a new face recognition method based on fuzzy kernel two-dimensional principal component analysis (FK2DPCA) is presented . Firstly, it introduces fuzzy concept into K2DPCA. Secondly, the class separability of criterion will be extended to high dimensional feature space by the use of kernel method. Furthermore, we select the eigenvectors that between-class scatter is greater than within-class scatter after projection as optimal projection axis. Finally, it uses the nearest neighbor classifier for face recognition . The experiment results on ORL and YALE face databases show that the FK2DPCA is better than other traditional methods.

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  - J.X. Zeng
AU  - P. Chen
AU  - J.Q. Tian
AU  - X. Fu
PY  - 2015/07
DA  - 2015/07
TI  - Fuzzy Kernel Two-dimensional Principal Component Analysis for Face Recognition
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 357
EP  - 360
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.99
DO  - 10.2991/aiie-15.2015.99
ID  - Zeng2015/07
ER  -