Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition

Lijun Yan; Shu-Chuan Chu; Jeng-Shyang Pan; John Roddick

doi:10.1109/IBICA.2011.91

Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition

Lijun Yan, Shu-Chuan Chu, Jeng-Shyang Pan, John Roddick

Research output: Contribution to conference › Paper › peer-review

18 Citations (Scopus)

Abstract

A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.

Original language	English
Pages	344-347
Number of pages	4
DOIs	https://doi.org/10.1109/IBICA.2011.91
Publication status	Published - 1 Dec 2011
Event	2nd International Conference on Innovations in Bio-inspired Computing and Applications - Duration: 16 Dec 2011 → …

Conference

Conference	2nd International Conference on Innovations in Bio-inspired Computing and Applications
Period	16/12/11 → …

Keywords

face
Nearest feature line
subspace learning

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10.1109/IBICA.2011.91

Cite this

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title = "Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition",

abstract = "A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.",

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AU - Chu, Shu-Chuan

AU - Pan, Jeng-Shyang

AU - Roddick, John

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Y1 - 2011/12/1

N2 - A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.

AB - A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.

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KW - Nearest feature line

KW - subspace learning

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T2 - 2nd International Conference on Innovations in Bio-inspired Computing and Applications

Y2 - 16 December 2011

ER -

Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition

Abstract

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Keywords

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