Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition

Jun Bao Li, Shu Chuan Chu, Jeng Shyang Pan

Research output: Contribution to conferencePaperpeer-review

Abstract

Dimensionality reduction is the most popular method for feature extraction and recognition. Recently, Li et al. (IEEE PAMI, 2009) proposed Nonparametric Discriminant Analysis (NDA) based dimensionality reduction for face recognition and reported an excellent recognition performance. However, NDA has its limitations on extracting the nonlinear features of face images for recognition, and owing to the highly nonlinear and complex distribution of face images under a perceivable variation in viewpoint, illumination or facial expression. In order to increase the NDA, we extend the NDA with kernel trick to propose Nonparametric Kernel Discriminant Analysis (NKDA) for feature extraction and recognition. Experimental results on ORL, YALE and UMIST face databases show that NKDA outperforms NDA on recognition, which demonstrates that it is feasible to improve NDA with kernel trick for feature extraction.

Original languageEnglish
Number of pages8
DOIs
Publication statusPublished - Jan 2010
Externally publishedYes
Event4th International Conference on Ubiquitous Information Management and Communication, ICUIMC'10 - Suwon, Korea, Republic of
Duration: 14 Jan 201015 Jan 2010

Conference

Conference4th International Conference on Ubiquitous Information Management and Communication, ICUIMC'10
CountryKorea, Republic of
CitySuwon
Period14/01/1015/01/10

Keywords

  • face recognition
  • kernel method
  • nonparametric discriminant analysis
  • nonparametric kernel discriminant analysis

Fingerprint Dive into the research topics of 'Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition'. Together they form a unique fingerprint.

Cite this