A New Kernel Orthogonal Projection Analysis Approach for Face Recognition
Xiaoyuan Jing, Min Li, Yongfang Yao, Songhao Zhu, Sheng Li
Available Online March 2013.
- https://doi.org/10.2991/iccsee.2013.727How to use a DOI?
- kernel orthogonal projection analysis, feature extraction, locally orthogonal constraints, face recognition
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - Conference 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 - https://doi.org/10.2991/iccsee.2013.727 ID - Jing2013/03 ER -