Kernel subspace LDA with optimized kernel parameters on face recognition

Jian Huang, Pong Chi YUEN, Wen Sheng Chen, J. H. Lai

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

47 Citations (Scopus)

Abstract

This paper addresses the problem of selection of Kernel parameters in Kernel Fisher Discriminant for face recognition. We propose a new criterion and derive a new formation in optimizing the parameters in RBF kernel based on the gradient descent algorithm. The proposed formulation is further integrated into a subspace LDA algorithm and a new face recognition algorithm is developed. FERET database is used for evaluation. Comparing with the existing Kernel LDA-based methods with kernel parameter selected by experiment manually, the results are encouraging.

Original languageEnglish
Title of host publicationProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
Pages327-332
Number of pages6
Publication statusPublished - 2004
EventProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004 - Seoul, Korea, Republic of
Duration: 17 May 200419 May 2004

Publication series

NameProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition

Conference

ConferenceProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
Country/TerritoryKorea, Republic of
CitySeoul
Period17/05/0419/05/04

Scopus Subject Areas

  • Engineering(all)

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