Recognizing Partially Damaged Facial Images by Subspace Auto-associative Memories

  • Xiaorong Pu
  • Zhang Yi
  • Yue Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


PCA and NMF subspace approaches have become the most representative methods in face recognition, which act in the similar way as a neural network auto-associative memory. By integrating with LDA subspace, in this paper, two subspace associative memories, PCA LDA and NMF LDA , are proposed, and how they recognize the partially damaged faces is presented. The theoretical expressions are plotted, and the comparative experiments are completed for the UMIST face database. It shows that NMF LDA subspace associative memory outperform PCA LDA subspace method significantly in recognizing partially damaged faces.


Face Recognition Linear Discriminant Analysis Facial Image Positive Matrix Factorization Sparseness Constraint 
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

  • Xiaorong Pu
    • 1
  • Zhang Yi
    • 1
  • Yue Wu
    • 1
  1. 1.Computational Intelligence Laboratory, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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