Globally maximizing, locally minimizing: Unsupervised discriminant projection with applications to face and palm biometrics

Jian Yang, Dapeng Zhang, Jing Yu Yang, Ben Niu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

462 Citations (Scopus)


This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, Locality Preserving Projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications.
Original languageEnglish
Pages (from-to)650-664
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number4
Publication statusPublished - 1 Apr 2007


  • Biometrics
  • Dimensionality reduction
  • Face recognition
  • Feature extraction
  • Fisher linear discriminant analysis (LDA)
  • Manifold learning
  • Palmprint recognition
  • Subspace learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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