Interpolatory Mercer kernel construction for kernel direct LDA on face recognition

Wen Sheng Chen*, Pong Chi YUEN

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

This paper proposes a novel methodology on Mercer kernel construction using interpolatory strategy. Based on a given symmetric and positive semi-definite matrix (Gram matrix) and Cholesky decomposition, it first constructs a nonlinear mapping Φ, which is well-defined on the training data. This mapping is then extended to the whole input feature space by utilizing Lagrange interpolatory basis functions. The kernel function constructed by inner product is proven to be a Mercer kernel function. The self-constructed interpolatory Mercer (IM) kernel keeps the Gram matrix unchanged on the training samples. To evaluate the performance of the proposed IM kernel, a popular kernel direct linear discriminant analysis (KDDA) method for face recognition is selected. Comparing with RBF kernel based KDDA method on two face databases, namely FERET and CMU PIE databases, the IMkernel based KDDA approach could increase the performance by around 20% on CMU PIE database.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages857-860
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: 19 Apr 200924 Apr 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan, Province of China
CityTaipei
Period19/04/0924/04/09

Scopus Subject Areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Face recognition
  • KDDA
  • Mercer kernel

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