Discriminative common vectors based on the Gram-Schmidt reorthogonalization for the small sample size problem

Ying Wen, Lianghua He, Yue Lu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The discriminative common vectors (DCV) algorithm shows better face recognition effects than some commonly used linear discriminant algorithms, which uses the subspace methods and the Gram-Schmidt orthogonalization (GSO) procedure to obtain the DCV. However, the Gram-Schmidt technique may produce a set of vectors which is far from orthogonal so that sometimes the orthogonality may be lost completely. Hence, the effectiveness of the DCV is also decreased. In this paper, we proposed an improved DCV method based on the GSO. For obtaining an accurate projection onto the corresponding space, the orthogonal basis problem is usually solved with the Gram-Schmidt process with reorthogonalization. Thus, the effectiveness of the DCV can be improved and the experimental results show that the proposed method is better for the small sample size problem as compared to the DCV.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1325-1328
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

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

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

Keywords

  • Discriminative common vector
  • Face recognition
  • Gram-Schmidt orthogonalization

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