Title :
Face Recognition Using Local Binary Decisions
Author :
James, Alex Pappachen ; Dimitrijev, Sima
Author_Institution :
Queensland Microtechnol. Facility & Griffith Sch. of Eng., Griffith Univ., Nathan, QLD
fDate :
6/30/1905 12:00:00 AM
Abstract :
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.
Keywords :
decision theory; face recognition; feature extraction; image classification; biologically inspired modular unit; face image; face recognition; human brain; natural variability; pixel-wise local binary decision; spatial-intensity change feature; Face recognition; Helium; Humans; Image databases; Image recognition; Image restoration; Logic; Neurons; Robustness; Testing; Image edge analysis; threshold logic;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2008.2006339
