Improving classification of neural networks by reducing lens aperture

Inna Stainvas*, Zeev Zalevsky, David Mendlovic, Nathan Intrator

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Image blur strongly degrades object recognition. We propose a mechanism to reduce defocus blur by reducing the aperture of the camera lens, and show that it leads to a far more robust recognition. The recognition is demonstrated via a Neural Network architecture that we have previously proposed for blurred face recognition.

Original languageEnglish
Pages (from-to)267-276
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2002
EventApplications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V - Seattle, WA, United States
Duration: 9 Jul 200210 Jul 2002


  • Artificial neural networks
  • Classification network
  • Face recognition
  • Hybrid architecture
  • Image blur
  • Lens aperture
  • Network ensembles


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