Learning neighborhood discriminative manifolds for video-based face recognition

John See*, Mohammad Faizal Ahmad Fauzi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


In this paper, we propose a new supervised Neighborhood Discriminative Manifold Projection (NDMP) method for feature extraction in video-based face recognition. The abundance of data in videos often result in highly nonlinear appearance manifolds. In order to extract good discriminative features, an optimal low-dimensional projection is learned from selected face exemplars by solving a constrained least-squares objective function based on both local neighborhood geometry and global manifold structure. The discriminative ability is enhanced through the use of intra-class and inter-class neighborhood information. Experimental results on standard video databases and comparisons with state-of-art methods demonstrate the capability of NDMP in achieving high recognition accuracy.

Original languageEnglish
Title of host publicationImage Analysis and Processing. ICIAP 2011
EditorsG. Maino, G. L. Foresti
Number of pages10
ISBN (Electronic)9783642240850
ISBN (Print)9783642240843
Publication statusPublished - 2011
Event16th International Conference on Image Analysis and Processing 2011 - Ravenna, Italy
Duration: 14 Sept 201116 Sept 2011

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Image Analysis and Processing 2011
Abbreviated titleICIAP 2011


  • feature extraction
  • Manifold learning
  • video-based face recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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