Median LDA: A robust feature extraction method for face recognition

Jian Yang, Dapeng Zhang, Jing Yu Yang

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


In the existing LDA models, class mean vector is always estimated by the class sample average. In small sample size problems such as face recognition, however, the class sample average does not suffice to provide an accurate estimate of the class mean based on a few of given samples, particularly when there are outliers in the sample set. To overcome this weakness, we use the class median vector to estimate the class mean vector in LDA modeling. The class median vector has two advantages over the class sample average: (1) the class median (image) vector preserves useful details in the sample images and (2) the class median vector is robust to outliers that exist in training sample set. The proposed median LDA model is evaluated using three popular face image databases. All experiment results indicate that median LDA is more effective than the common LDA and PCA.
Original languageEnglish
Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
Number of pages6
Publication statusPublished - 28 Aug 2007
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan
Duration: 8 Oct 200611 Oct 2006


Conference2006 IEEE International Conference on Systems, Man and Cybernetics

ASJC Scopus subject areas

  • Engineering(all)

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