Kernel pca with doubly nonlinear mapping for face recognition

Xudong Xie, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, a novel Gabor-based kernel Principal Component Analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA is devised to perform feature transformation and face recognition. Our algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial (FPP) models. Experiments show that consistent and promising results are obtained.
Original languageEnglish
Title of host publicationProceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
Pages73-76
Number of pages4
Volume2005
Publication statusPublished - 1 Dec 2005
Event2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 - Hong Kong, Hong Kong
Duration: 13 Dec 200516 Dec 2005

Conference

Conference2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
CountryHong Kong
CityHong Kong
Period13/12/0516/12/05

ASJC Scopus subject areas

  • Engineering(all)

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