Wavelet kernel construction for kernel discriminant analysis on face recognition

Wen Sheng Chen; Pong Chi Yuen; Jian Huang; Jianghuang Lai

doi:10.1109/CVPRW.2006.213

Wavelet kernel construction for kernel discriminant analysis on face recognition

Wen Sheng Chen^*, Pong Chi Yuen, Jian Huang, Jianghuang Lai

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

6 Citations (Scopus)

Abstract

Kernel Discriminant Analysis (KDA) has been shown to be one of the promising approaches to handle the pose and illumination problem in face recognition. However, empirical results show that the performance for KDA on face recognition is sensitive to the kernel function and its parameters. Instead of following existing KDA methods in selecting popular kernel function, this paper proposes a new approach for constructing kernel using wavelet. By virtue of cubic B spline function, wavelet kernel function is constructed. A wavelet kernel based subspace linear discriminant (WKSLDA) algorithm is then developed for face recognition. Two human face databases, namely FERET and CMU PIE databases, are selected for evaluation. The results are encouraging. Comparing with the existing state-of-the-art RBF kernel based LDA methods, the proposed method gives superior results.

Original language	English
Title of host publication	2006 Conference on Computer Vision and Pattern Recognition Workshop
DOIs	https://doi.org/10.1109/CVPRW.2006.213
Publication status	Published - 2006
Event	2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States Duration: 17 Jun 2006 → 22 Jun 2006

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2006
ISSN (Print)	1063-6919

Conference

Conference	2006 Conference on Computer Vision and Pattern Recognition Workshops
Country/Territory	United States
City	New York, NY
Period	17/06/06 → 22/06/06

Access to Document

10.1109/CVPRW.2006.213

Cite this

Chen, W. S., Yuen, P. C., Huang, J., & Lai, J. (2006). Wavelet kernel construction for kernel discriminant analysis on face recognition. In 2006 Conference on Computer Vision and Pattern Recognition Workshop Article 1640487 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2006). https://doi.org/10.1109/CVPRW.2006.213

@inproceedings{74cee4fc2faf4a018de5dcd7e2fbcf50,

title = "Wavelet kernel construction for kernel discriminant analysis on face recognition",

abstract = "Kernel Discriminant Analysis (KDA) has been shown to be one of the promising approaches to handle the pose and illumination problem in face recognition. However, empirical results show that the performance for KDA on face recognition is sensitive to the kernel function and its parameters. Instead of following existing KDA methods in selecting popular kernel function, this paper proposes a new approach for constructing kernel using wavelet. By virtue of cubic B spline function, wavelet kernel function is constructed. A wavelet kernel based subspace linear discriminant (WKSLDA) algorithm is then developed for face recognition. Two human face databases, namely FERET and CMU PIE databases, are selected for evaluation. The results are encouraging. Comparing with the existing state-of-the-art RBF kernel based LDA methods, the proposed method gives superior results.",

author = "Chen, {Wen Sheng} and Yuen, {Pong Chi} and Jian Huang and Jianghuang Lai",

year = "2006",

doi = "10.1109/CVPRW.2006.213",

language = "English",

isbn = "0769526462",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

booktitle = "2006 Conference on Computer Vision and Pattern Recognition Workshop",

}

Chen, WS, Yuen, PC, Huang, J & Lai, J 2006, Wavelet kernel construction for kernel discriminant analysis on face recognition. in 2006 Conference on Computer Vision and Pattern Recognition Workshop., 1640487, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshops, New York, NY, United States, 17/06/06. https://doi.org/10.1109/CVPRW.2006.213

Wavelet kernel construction for kernel discriminant analysis on face recognition. / Chen, Wen Sheng; Yuen, Pong Chi; Huang, Jian et al.
2006 Conference on Computer Vision and Pattern Recognition Workshop. 2006. 1640487 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2006).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Wavelet kernel construction for kernel discriminant analysis on face recognition

AU - Chen, Wen Sheng

AU - Yuen, Pong Chi

AU - Huang, Jian

AU - Lai, Jianghuang

PY - 2006

Y1 - 2006

N2 - Kernel Discriminant Analysis (KDA) has been shown to be one of the promising approaches to handle the pose and illumination problem in face recognition. However, empirical results show that the performance for KDA on face recognition is sensitive to the kernel function and its parameters. Instead of following existing KDA methods in selecting popular kernel function, this paper proposes a new approach for constructing kernel using wavelet. By virtue of cubic B spline function, wavelet kernel function is constructed. A wavelet kernel based subspace linear discriminant (WKSLDA) algorithm is then developed for face recognition. Two human face databases, namely FERET and CMU PIE databases, are selected for evaluation. The results are encouraging. Comparing with the existing state-of-the-art RBF kernel based LDA methods, the proposed method gives superior results.

AB - Kernel Discriminant Analysis (KDA) has been shown to be one of the promising approaches to handle the pose and illumination problem in face recognition. However, empirical results show that the performance for KDA on face recognition is sensitive to the kernel function and its parameters. Instead of following existing KDA methods in selecting popular kernel function, this paper proposes a new approach for constructing kernel using wavelet. By virtue of cubic B spline function, wavelet kernel function is constructed. A wavelet kernel based subspace linear discriminant (WKSLDA) algorithm is then developed for face recognition. Two human face databases, namely FERET and CMU PIE databases, are selected for evaluation. The results are encouraging. Comparing with the existing state-of-the-art RBF kernel based LDA methods, the proposed method gives superior results.

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U2 - 10.1109/CVPRW.2006.213

DO - 10.1109/CVPRW.2006.213

M3 - Conference proceeding

AN - SCOPUS:33845526008

SN - 0769526462

SN - 9780769526461

T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

BT - 2006 Conference on Computer Vision and Pattern Recognition Workshop

T2 - 2006 Conference on Computer Vision and Pattern Recognition Workshops

Y2 - 17 June 2006 through 22 June 2006

ER -

Wavelet kernel construction for kernel discriminant analysis on face recognition

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