An efficient reformative kernel discriminant Analysis for face recognition

Jun Bao Li; Jeng Shyang Pan; Zhe Ming Lu

doi:10.1109/ROBIO.2006.340211

An efficient reformative kernel discriminant Analysis for face recognition

Jun Bao Li^*
, Jeng Shyang Pan
, Zhe Ming Lu

^*Corresponding author for this work

Faculty of Computing

National Kaohsiung University of Science and Technology
Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

An efficient reformative kernel discriminant Analysis, namely Enhanced Kernel Discriminant Analysis (EKDA), is proposed in this paper. In the proposed algorithm, a novel criterion, i.e., maximizing the class separability both in the feature space and in the projection subspace, is presented to enhance the discriminant power of KDA. EKDA is more adaptive to the input data under the novel criterion compared with KDA, which enhances the performance of EKDA. Experiments conducted on the Yale and ORL face databases give the higher recognition performance compared with KDA.

Original language	English
Title of host publication	2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006
Pages	406-409
Number of pages	4
DOIs	https://doi.org/10.1109/ROBIO.2006.340211
State	Published - 2006
Event	2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006 - Kunming, China Duration: 17 Dec 2006 → 20 Dec 2006

Publication series

Name	2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006

Conference

Conference	2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006
Country/Territory	China
City	Kunming
Period	17/12/06 → 20/12/06

Keywords

Enhanced kernel discriminant analysis (EKDA)
Face recognition
Kernel discriminant analysis
Kernel optimization

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10.1109/ROBIO.2006.340211

Cite this

@inproceedings{27ac8b86d5094bf585625f0c0dae238a,

title = "An efficient reformative kernel discriminant Analysis for face recognition",

abstract = "An efficient reformative kernel discriminant Analysis, namely Enhanced Kernel Discriminant Analysis (EKDA), is proposed in this paper. In the proposed algorithm, a novel criterion, i.e., maximizing the class separability both in the feature space and in the projection subspace, is presented to enhance the discriminant power of KDA. EKDA is more adaptive to the input data under the novel criterion compared with KDA, which enhances the performance of EKDA. Experiments conducted on the Yale and ORL face databases give the higher recognition performance compared with KDA.",

keywords = "Enhanced kernel discriminant analysis (EKDA), Face recognition, Kernel discriminant analysis, Kernel optimization",

author = "Li, \{Jun Bao\} and Pan, \{Jeng Shyang\} and Lu, \{Zhe Ming\}",

year = "2006",

doi = "10.1109/ROBIO.2006.340211",

language = "英语",

isbn = "1424405718",

series = "2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006",

pages = "406--409",

booktitle = "2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006",

note = "2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006 ; Conference date: 17-12-2006 Through 20-12-2006",

}

Li, JB, Pan, JS & Lu, ZM 2006, An efficient reformative kernel discriminant Analysis for face recognition. in 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006., 4141900, 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006, pp. 406-409, 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006, Kunming, China, 17/12/06. https://doi.org/10.1109/ROBIO.2006.340211

An efficient reformative kernel discriminant Analysis for face recognition. / Li, Jun Bao; Pan, Jeng Shyang; Lu, Zhe Ming.
2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006. 2006. p. 406-409 4141900 (2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An efficient reformative kernel discriminant Analysis for face recognition

AU - Li, Jun Bao

AU - Pan, Jeng Shyang

AU - Lu, Zhe Ming

PY - 2006

Y1 - 2006

N2 - An efficient reformative kernel discriminant Analysis, namely Enhanced Kernel Discriminant Analysis (EKDA), is proposed in this paper. In the proposed algorithm, a novel criterion, i.e., maximizing the class separability both in the feature space and in the projection subspace, is presented to enhance the discriminant power of KDA. EKDA is more adaptive to the input data under the novel criterion compared with KDA, which enhances the performance of EKDA. Experiments conducted on the Yale and ORL face databases give the higher recognition performance compared with KDA.

AB - An efficient reformative kernel discriminant Analysis, namely Enhanced Kernel Discriminant Analysis (EKDA), is proposed in this paper. In the proposed algorithm, a novel criterion, i.e., maximizing the class separability both in the feature space and in the projection subspace, is presented to enhance the discriminant power of KDA. EKDA is more adaptive to the input data under the novel criterion compared with KDA, which enhances the performance of EKDA. Experiments conducted on the Yale and ORL face databases give the higher recognition performance compared with KDA.

KW - Enhanced kernel discriminant analysis (EKDA)

KW - Face recognition

KW - Kernel discriminant analysis

KW - Kernel optimization

UR - https://www.scopus.com/pages/publications/46249112457

U2 - 10.1109/ROBIO.2006.340211

DO - 10.1109/ROBIO.2006.340211

M3 - 会议稿件

AN - SCOPUS:46249112457

SN - 1424405718

SN - 9781424405718

T3 - 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006

SP - 406

EP - 409

BT - 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006

T2 - 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006

Y2 - 17 December 2006 through 20 December 2006

ER -

An efficient reformative kernel discriminant Analysis for face recognition

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Keywords

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