Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31263
Title: Face recognition based on a novel linear discriminant criterion
Authors: Song, F
Zhang, D 
Chen, Q
Wang, J
Keywords: Binary linear classifier
Face recognition
Fisher linear discriminant
Multi-objective programming
Pattern classification
Small sample size problem
Issue Date: 2007
Source: Pattern analysis and applications, 2007, v. 10, no. 3, p. 165-174 How to cite?
Journal: Pattern Analysis and Applications 
Abstract: As an effective technique for feature extraction and pattern classification Fisher linear discriminant (FLD) has been successfully applied in many fields. However, for a task with very high-dimensional data such as face images, conventional FLD technique encounters a fundamental difficulty caused by singular within-class scatter matrix. To avoid the trouble, many improvements on the feature extraction aspect of FLD have been proposed. In contrast, studies on the pattern classification aspect of FLD are quiet few. In this paper, we will focus our attention on the possible improvement on the pattern classification aspect of FLD by presenting a novel linear discriminant criterion called maximum scatter difference (MSD). Theoretical analysis demonstrates that MSD criterion is a generalization of Fisher discriminant criterion, and is the asymptotic form of discriminant criterion: large margin linear projection. The performance of MSD classifier is tested in face recognition. Experiments performed on the ORL, Yale, FERET and AR databases show that MSD classifier can compete with top-performance linear classifiers such as linear support vector machines, and is better than or equivalent to combinations of well known facial feature extraction methods, such as eigenfaces, Fisherfaces, orthogonal complementary space, nullspace, direct linear discriminant analysis, and the nearest neighbor classifier.
URI: http://hdl.handle.net/10397/31263
ISSN: 1433-7541
DOI: 10.1007/s10044-006-0057-3
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

16
Last Week
0
Last month
1
Citations as of Oct 11, 2017

WEB OF SCIENCETM
Citations

14
Last Week
0
Last month
0
Citations as of Oct 16, 2017

Page view(s)

39
Last Week
1
Last month
Checked on Oct 16, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.