Ensemble-Based Discriminant Manifold Learning for Face Recognition

  • Junping Zhang
  • Li He
  • Zhi-Hua Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


The locally linear embedding (LLE) algorithm can be used to discover a low-dimensional subspace from face manifolds. However, it does not mean that a good accuracy can be obtained when classifiers work under the subspace. Based on the proposed ULLELDA (Unified LLE and linear discriminant analysis) algorithm, an ensemble version of the ULLELDA (En-ULLELDA) is proposed by perturbing the neighbor factors of the LLE algorithm. Here many component learners are generated, each of which produces a single face subspace through some neighborhood parameter of the ULLELDA algorithm and is trained by a classifier. The classification results of these component learners are then combined through majority voting to produce the final prediction. Experiments on several face databases show the promising of the En-ULLELDA algorithm.


Face Recognition Linear Discriminant Analysis Face Database Locally Linear Embedding Neighbor Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Junping Zhang
    • 1
    • 2
  • Li He
    • 1
  • Zhi-Hua Zhou
    • 3
  1. 1.Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and EngineeringFudan UniversityShanghaiChina
  2. 2.The Key Laboratory of Complex Systems and Intelligence ScienceInstitute of Automation, Chinese Academy of SciencesBeijingChina
  3. 3.National Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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