Modular dynamic RBF neural network for face recognition

Sue Inn Ch'Ng*, Kah Phooi Seng, Li Minn Ang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

8 Citations (Scopus)


Over the years, we have seen an increase in the use of RBF neural networks for the task of face recognition. However, the use of second order algorithms as the learning algorithm for all the adjustable parameters in such networks are rare due to the high computational complexity of the calculation of the Jacobian and Hessian matrix. Hence, in this paper, we propose a modular structural training architecture to adapt the Levenberg-Marquardt based RBF neural network for the application of face recognition. In addition to the proposal of the modular structural training architecture, we have also investigated the use of different front-end processors to reduce the dimension size of the feature vectors prior to its application to the LM-based RBF neural network. The investigative study was done on three standard face databases; ORL, Yale and AR databases.

Original languageEnglish
Title of host publication2012 IEEE Conference on Open Systems, ICOS 2012
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE Conference on Open Systems, ICOS 2012 - Kuala Lumpur, Malaysia
Duration: 21 Oct 201224 Oct 2012

Publication series

Name2012 IEEE Conference on Open Systems, ICOS 2012


Conference2012 IEEE Conference on Open Systems, ICOS 2012
CityKuala Lumpur


  • Levenberg-Marquardt algorithm
  • RBF neural networks
  • face recognition
  • modular structure

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