Tangent space discriminant analysis for feature extraction

Zhihui Lai, Zhong Jin, Wai Keung Wong

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

3 Citations (Scopus)


In this paper, a novel method called tangent space discriminant analysis is proposed for dimensionality reduction and feature extraction. Differing from the recently proposed manifold learning methods completely operating on raw feature space, TSDA completely uses the local tangent space to represent the local within-class geometry and local between-class geometry. Assume that the face images of different people reside on different intrinsically low-dimensional sub-manifolds, TSDA is developed to preserve the locality of each sub-manifold and simultaneously maximize the local separability of different sub-manifolds by using local tangent space alignment. Experimental results show that TSDA achieves higher recognition rates than a few the state-of-the-art techniques.
Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Number of pages4
Publication statusPublished - 1 Dec 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sept 201029 Sept 2010


Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong


  • Face recogntion
  • Feature extraction
  • Local tangent space alignment
  • Manifold learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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