A New Kernel Orthogonal Projection Analysis Approach for Face Recognition

DOI

10.2991/iccsee.2013.727How to use a DOI?

Keywords

kernel orthogonal projection analysis, feature extraction, locally orthogonal constraints, face recognition

Abstract

In the field of face recognition, how to extract effective nonlinear discriminative features is an important research topic. In this paper, we propose a new kernel orthogonal projection analysis approach. We obtain the optimal nonlinear projective vector which can differentiate one class and its adjacent classes, by using the Fisher criterion and constructing the specific between-class and within-class scatter matrices in kernel space. In addition, to eliminate the redundancy among projective vectors, our approach makes every projective vector satisfy locally orthogonal constraints by using the corresponding class and part of its most adjacent classes. Experimental results on the public AR and CAS-PEAL face databases demonstrate that the proposed approach outperforms several representative nonlinear projection analysis methods.

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  - Xiaoyuan Jing
AU  - Min Li
AU  - Yongfang Yao
AU  - Songhao Zhu
AU  - Sheng Li
PY  - 2013/03
DA  - 2013/03
TI  - A New Kernel Orthogonal Projection Analysis Approach for Face Recognition
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2914
EP  - 2917
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.727
DO  - 10.2991/iccsee.2013.727
ID  - Jing2013/03
ER  -