Rank-lifting strategy based kernel regularized discriminant analysis method for face recognition

Wen Sheng Chen*, Pong Chi YUEN, Xuehui Xie

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

To address Small Sample Size (S3) problem and nonlinear problem of face recognition, this paper proposes a novel rank-lifting based kernel regularized discriminant analysis method (RL-KRDA). It first proves a rank-lifting theorem using algebraic theory. Combining a new ranklifting strategy with standby three-to-one regularization technique, the complete regularized technology is developed on the within-class scatter matrix Sw. Our regularized scheme not only adjusts the projection directions but tunes their corresponding weights as well. It is also shown that the final regularized within-class scatter matrix approaches to the original one as the regularized parameters tend to zeros. The public available database, i.e. CMU PIE face database, is selected for evaluation. Comparing with some existing kernel-based LDA methods for solving S3 problem, the proposed RL-KRDA approach gives the best performance.

Original languageEnglish
Title of host publication2010 Chinese Conference on Pattern Recognition, CCPR 2010 - Proceedings
Pages141-145
Number of pages5
DOIs
Publication statusPublished - 2010
Event2010 Chinese Conference on Pattern Recognition, CCPR 2010 - Chongqing, China
Duration: 21 Oct 201023 Oct 2010

Publication series

Name2010 Chinese Conference on Pattern Recognition, CCPR 2010 - Proceedings

Conference

Conference2010 Chinese Conference on Pattern Recognition, CCPR 2010
Country/TerritoryChina
CityChongqing
Period21/10/1023/10/10

Scopus Subject Areas

  • Computer Vision and Pattern Recognition

User-Defined Keywords

  • Face recognition
  • Kernel method
  • Rank lifting scheme
  • Small sample size problem

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