Kernel fukunaga-koontz transform subspaces for enhanced face recognition

Yung Hui Li, Marios Savvides

研究成果: 書貢獻/報告類型會議論文篇章同行評審

16 引文 斯高帕斯(Scopus)

摘要

Traditional linear Fukunaga-Koontz Transform (FKT) [1] is a powerful discriminative subspaces building approach. Previous work has successfully extended FKT to be able to deal with small-sample-size. In this paper, we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional (kernel) subspaces and therefore provide enhanced discrimination ability. We verify the effectiveness of the proposed Kernel Fukunaga-Koontz Transform by demonstrating its effectiveness in face recognition applications; however the proposed non-linear generalization can be applied to any other domain specific problems.

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主出版物標題2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
出版狀態已出版 - 2007
事件2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
持續時間: 17 6月 200722 6月 2007

出版系列

名字Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN(列印)1063-6919

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???event.eventtypes.event.conference???2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
國家/地區United States
城市Minneapolis, MN
期間17/06/0722/06/07

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