Face Recognition Using Double Sparse Local Fisher Discriminant Analysis
Joint Authors
Ruan, Qiuqi
An, Gaoyun
Wang, Zhan
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Local Fisher discriminant analysis (LFDA) was proposed for dealing with the multimodal problem.
It not only combines the idea of locality preserving projections (LPP) for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA) for obtaining the discriminant power.
However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods.
Meanwhile, the projection matrix is not sparse.
In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA) for face recognition.
The proposed method firstly constructs a sparse and data-adaptive graph with nonnegative constraint.
Then, DSLFDA reformulates the objective function as a regression-type optimization problem.
The undersampled problem is avoided naturally and the sparse solution can be obtained by adding the regression-type problem to a l 1 penalty.
Experiments on Yale, ORL, and CMU PIE face databases are implemented to demonstrate the effectiveness of the proposed method.
American Psychological Association (APA)
Wang, Zhan& Ruan, Qiuqi& An, Gaoyun. 2015. Face Recognition Using Double Sparse Local Fisher Discriminant Analysis. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1074352
Modern Language Association (MLA)
Wang, Zhan…[et al.]. Face Recognition Using Double Sparse Local Fisher Discriminant Analysis. Mathematical Problems in Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1074352
American Medical Association (AMA)
Wang, Zhan& Ruan, Qiuqi& An, Gaoyun. Face Recognition Using Double Sparse Local Fisher Discriminant Analysis. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1074352
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074352