Mutual neighborhood based discriminant projection for face recognition

Ben Niu, Chi Keung Simon Shiu, Sankar Pal

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

1 Citation (Scopus)


Linear Discriminant Analysis is optimal under the assumption that the covariance matrices of the conditional densities are normal and all identical. However, this doesn't hold for many real world applications, such as Facial Image Recognition, in which data are typically under-sampled and non-Gaussian. To address this deficiency the Non-Parametric Discriminant method has been developed, but it requires model selection to be carried out for selecting the free control parameters, making it not easy for use in practice. We proposed a method, Mutual Neighborhood based Discriminant Projection, to overcome this problem. MNDP identifies the samples that contribute most to the Baysesian errors and highlights them for optimization. It is more convenient for use than NDA and avoids the singularity problem of LDA. On facial image datasets MNDP is shown to outperform Eigenfaces and Fisherfaces under various experimental conditions.
Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings
Number of pages6
Publication statusPublished - 1 Dec 2009
Event3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009 - New Delhi, India
Duration: 16 Dec 200920 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5909 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009
CityNew Delhi


  • Discriminant projection
  • Face recognition
  • K-nearest neighbors
  • Mutual neighborhood

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science


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