Two dimension locality preserving projections with class information for face recognition

The dimension reduction is necessary steps for face recognition based on subspace analysis. The proposed method employs class information for structure of similarity matrix when implement of 2DLPP. A subspace which preserves local neighbor structure and centralizes same class samples of training ima...

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Bibliographic Details
Published in2009 2nd IEEE International Conference on Computer Science and Information Technology pp. 249 - 252
Main Authors Yang Jun, Gao Zhi-Sheng, Zhang Xiu-Qiong, Yuan Hong-Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
2DLPP
class information
Computer science
Data mining
dimension reduction
Educational institutions
Face detection
Face recognition
Feature extraction
Image databases
Linear discriminant analysis
Principal component analysis
Scattering
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Summary:The dimension reduction is necessary steps for face recognition based on subspace analysis. The proposed method employs class information for structure of similarity matrix when implement of 2DLPP. A subspace which preserves local neighbor structure and centralizes same class samples of training images is got. Moreover, it has available computation efficiency and accuracy because it belongs to the methods based on images which avoid the matrix singularity problem. The performance of the proposed method is evaluated and compared with other popular subspace analysis method based on ORL database. The experiment results show that it has more accurate recognition than previous methods.
ISBN:1424445191
9781424445196
DOI:10.1109/ICCSIT.2009.5234573