A novel subspace LDA algorithm for recognition of face images with illumination and pose variations

Jian Huang*, Pong Chi YUEN, Wen Sheng Chen

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

This paper addresses two LDA problems in face recognition. The first one is small sample size (S3) problem while the second is illumination and pose variations. To overcome the S3 problem, this paper proposes a new method in subspace approach in determining the optimal projection for LDA. Also, an in-depth investigation is conducted on the influence of different illuminations and poses variations. Comparisons with existing LDA-based methods are performed using FERET and Yale Group B face databases. The experimental results show that the proposed method gives the best performance comparing with the existing LDA-based methods for both databases. Moreover, the computational cost of the proposed method is near the same as the existing fastest LDA-based method.

Original languageEnglish
Title of host publicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages3589-3594
Number of pages6
Publication statusPublished - 2004
EventProceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China
Duration: 26 Aug 200429 Aug 2004

Publication series

NameProceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume6

Conference

ConferenceProceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityShanghai
Period26/08/0429/08/04

Scopus Subject Areas

  • Engineering(all)

Fingerprint

Dive into the research topics of 'A novel subspace LDA algorithm for recognition of face images with illumination and pose variations'. Together they form a unique fingerprint.

Cite this