Skip to main content
SpringerLink
Log in

Recognizing Partially Damaged Facial Images by Subspace Auto-associative Memories

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Included in the following conference series:

  • 89 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Turk, M.A., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neurosience 3, 71–86 (1991)

    Article  Google Scholar 

  2. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Anal. Machine Intell. 19, 711–720 (1997)

    Article  Google Scholar 

  3. Patrik, O.H.: Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)

    Google Scholar 

  4. Lee, D.D., Seung, H.S.: Learning the Parts of Objects by Non-negative Matrix Factorization. Nature 401(6755), 788–791 (1999)

    Article  Google Scholar 

  5. Valentin, D., Abdi, H., O’Toole, A.J.: Categorization and Identification of Human Face Images by Neural Networks: A Review of the Linear Autoassociative and Principle Component Approaches. Journal of Biological Systems 2(3), 413–429 (1994)

    Article  Google Scholar 

  6. Yambor, W.S.: Analysis of PCA-Based and Fisher Discriminant-based Image Recognition Algorithms. Technical Report CS-00-103, Colorado State University (2000)

    Google Scholar 

  7. Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)

    Article  Google Scholar 

  8. Pu, X., Zhang, Y., et al.: Face Recognition Using Fisher Non-negative Matrix Factorization with Sparseness Constraints. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 112–117. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Hagan, M.T., et al.: Neural Network Design. PWS Publishing Company (1996)

    Google Scholar 

  10. The UMIST database, http://images.ee.umist.ac.uk/danny/database.html

  11. Paatero, P., Tapper, U.: Positive Matrix Factorization- A Non-negative Factor Model with Optimal Utilization of Error Estimates of Data Values. Environmetrics 5, 111–126 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Xiaorong Pu, Zhang Yi & Yue Wu

Authors
  1. Xiaorong Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pu, X., Yi, Z., Wu, Y. (2006). Recognizing Partially Damaged Facial Images by Subspace Auto-associative Memories. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_8

Download citation

  • DOI: https://doi.org/10.1007/11760023_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation