Wavelet kernel construction for kernel discriminant analysis on face recognition

Wen Sheng Chen*, Pong Chi YUEN, Jian Huang, Jianghuang Lai

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Kernel Discriminant Analysis (KDA) has been shown to be one of the promising approaches to handle the pose and illumination problem in face recognition. However, empirical results show that the performance for KDA on face recognition is sensitive to the kernel function and its parameters. Instead of following existing KDA methods in selecting popular kernel function, this paper proposes a new approach for constructing kernel using wavelet. By virtue of cubic B spline function, wavelet kernel function is constructed. A wavelet kernel based subspace linear discriminant (WKSLDA) algorithm is then developed for face recognition. Two human face databases, namely FERET and CMU PIE databases, are selected for evaluation. The results are encouraging. Comparing with the existing state-of-the-art RBF kernel based LDA methods, the proposed method gives superior results.

Original languageEnglish
Title of host publication2006 Conference on Computer Vision and Pattern Recognition Workshop
DOIs
Publication statusPublished - 2006
Event2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States
Duration: 17 Jun 200622 Jun 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2006
ISSN (Print)1063-6919

Conference

Conference2006 Conference on Computer Vision and Pattern Recognition Workshops
Country/TerritoryUnited States
CityNew York, NY
Period17/06/0622/06/06

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition

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