DocumentCode :
3496290
Title :
Face identification using linear regression
Author :
Naseem, A. Imran ; Togneri, B. Roberto ; Bennamoun, C. Mohammed
Author_Institution :
Sch. of EECE, Univ. of Western Australia, Crawley, WA, Australia
fYear :
2009
fDate :
7-10 Nov. 2009
Firstpage :
4161
Lastpage :
4164
Abstract :
In this paper we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class specific galleries. The inverse problem is solved using the least squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The algorithm is extensively evaluated using two standard databases, a comparative study with the benchmark algorithms clearly reflects the efficacy of the proposed approach.
Keywords :
face recognition; least squares approximations; regression analysis; face identification; least squares method; linear regression; linear subspace; minimum reconstruction error; pattern recognition problem; rithms; Australia; Face recognition; Gray-scale; Image reconstruction; Linear regression; Pattern recognition; Pollution measurement; Probes; Road transportation; Vectors; Linear regression; face recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
ISSN :
1522-4880
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2009.5414539
Filename :
5414539
Link To Document :
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