A new face recognition system based on Kernel maximum between-class margin criterion (KMMC)
Minghua Wan, Qiaoli Wang, Hui Liu, Xun Li
Available Online April 2015.
- https://doi.org/10.2991/icmra-15.2015.117How to use a DOI?
- Face recognition; Feature extraction; Small sample size; Kernel maximum between-class margin criterion
- To avoid small sample problem in pattern recognition, the paper uses KMMC (Kernel maximum between-class margin criterion method) as the basic extraction method for face recognition, which is based on the maximum difference of between-class scatter and within-class scatter in feature space. The objective of KMMC is to seek an optimal set of discriminant vectors as the projection axis to do some projection transformation, and to make the between-class scatter of feature space sample maximum, the within-class scatter minimum, theoretically solved the problems that can not be solved due to singularity of within-class scatter and demonstrates its efficiency of feature extraction furthermore. The test results show the validity of this method on ORL database. At last, it designed and implemented face recognition system based on KMMC by using Matlab7.1.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Minghua Wan AU - Qiaoli Wang AU - Hui Liu AU - Xun Li PY - 2015/04 DA - 2015/04 TI - A new face recognition system based on Kernel maximum between-class margin criterion (KMMC) BT - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation PB - Atlantis Press SP - 602 EP - 606 SN - 2352-538X UR - https://doi.org/10.2991/icmra-15.2015.117 DO - https://doi.org/10.2991/icmra-15.2015.117 ID - Wan2015/04 ER -