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

Volume

15

fYear

2008

fDate

6/30/1905 12:00:00 AM

Firstpage

821

Lastpage

824

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;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2008.2006339

Filename

4682537