Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics
Joint Authors
Lin, Chuang
Pang, Meng
Jiang, Jifeng
Ma, Yanchun
Zhao, Xue-Feng
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Kernel Locality Preserving Projection (KLPP) algorithm can effectively preserve the neighborhood structure of the database using the kernel trick.
We have known that supervised KLPP (SKLPP) can preserve within-class geometric structures by using label information.
However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP.
In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP) is proposed in this paper, which can maximize the class separability in kernel learning.
The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel.
The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function.
Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data.
Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.
American Psychological Association (APA)
Lin, Chuang& Jiang, Jifeng& Zhao, Xue-Feng& Pang, Meng& Ma, Yanchun. 2015. Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1073799
Modern Language Association (MLA)
Lin, Chuang…[et al.]. Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics. Mathematical Problems in Engineering No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1073799
American Medical Association (AMA)
Lin, Chuang& Jiang, Jifeng& Zhao, Xue-Feng& Pang, Meng& Ma, Yanchun. Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1073799
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073799