Face recognition using local binary decisions

Alex Pappachen James, Sima Dimitrijev

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


The human brain exhibits robustness against natural variability occurring in face images, yet the commonly attempted algorithms for face recognition are not modular and do not apply the principle of binary decisions made by the firing of neurons. We present a biologically inspired modular unit implemented as an algorithm for face recognition that applies pixel-wise local binary decisions on similarity of spatial-intensity change features. The results obtained with a single gallery image per person show a robust and high recognition performance: 94% on AR, 98% on Yale, 97% on ORL, 97% on FERET (fb), 92% on FERET (fc), and 96% on Caltech face image databases.

Original languageEnglish
Pages (from-to)821-824
Number of pages4
JournalIEEE Signal Processing Letters
Publication statusPublished - 2008


  • Image edge analysis
  • Threshold logic

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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