Two dimensional Laplacianfaces method for face recognition

Niu Ben; Chi Keung Simon Shiu; Sankar Kumar Pal

Two dimensional Laplacianfaces method for face recognition

Niu Ben, Chi Keung Simon Shiu, Sankar Kumar Pal

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

In this paper we propose the two dimensional Laplacianfaces method for face recognition. The new algorithm is developed based on the two techniques, i.e., locality preserved embedding and image based projection. The two dimensional Laplacianfaces method is not only computationally more efficient but also more accurate than the one dimensional Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the Yale and the AR face databases. The experimental results indicate that the two dimensional Laplacianfaces method significantly outperforms the existing two dimensional Eigenfaces, the two dimensional Fisherfaces and the one dimensional Laplacianfaces methods under the various settings of experiment conditions.

Original language	English
Title of host publication	Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings
Publisher	Springer Verlag
Pages	852-861
Number of pages	10
ISBN (Print)	3540476938, 9783540476931
Publication status	Published - 1 Jan 2006
Event	5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006 - Kobe, Japan Duration: 6 Nov 2006 → 8 Nov 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4259 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006
Country/Territory	Japan
City	Kobe
Period	6/11/06 → 8/11/06

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Cite this

Ben, N., Shiu, C. K. S., & Pal, S. K. (2006). Two dimensional Laplacianfaces method for face recognition. In Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings (pp. 852-861). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4259 LNAI). Springer Verlag.

Ben, Niu ; Shiu, Chi Keung Simon ; Pal, Sankar Kumar. / Two dimensional Laplacianfaces method for face recognition. Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings. Springer Verlag, 2006. pp. 852-861 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Two dimensional Laplacianfaces method for face recognition",

abstract = "In this paper we propose the two dimensional Laplacianfaces method for face recognition. The new algorithm is developed based on the two techniques, i.e., locality preserved embedding and image based projection. The two dimensional Laplacianfaces method is not only computationally more efficient but also more accurate than the one dimensional Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the Yale and the AR face databases. The experimental results indicate that the two dimensional Laplacianfaces method significantly outperforms the existing two dimensional Eigenfaces, the two dimensional Fisherfaces and the one dimensional Laplacianfaces methods under the various settings of experiment conditions.",

author = "Niu Ben and Shiu, {Chi Keung Simon} and Pal, {Sankar Kumar}",

year = "2006",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings",

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note = "5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006 ; Conference date: 06-11-2006 Through 08-11-2006",

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Ben, N, Shiu, CKS & Pal, SK 2006, Two dimensional Laplacianfaces method for face recognition. in Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4259 LNAI, Springer Verlag, pp. 852-861, 5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006, Kobe, Japan, 6/11/06.

Two dimensional Laplacianfaces method for face recognition. / Ben, Niu; Shiu, Chi Keung Simon; Pal, Sankar Kumar.

Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings. Springer Verlag, 2006. p. 852-861 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4259 LNAI).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Two dimensional Laplacianfaces method for face recognition

AU - Ben, Niu

AU - Shiu, Chi Keung Simon

AU - Pal, Sankar Kumar

PY - 2006/1/1

Y1 - 2006/1/1

N2 - In this paper we propose the two dimensional Laplacianfaces method for face recognition. The new algorithm is developed based on the two techniques, i.e., locality preserved embedding and image based projection. The two dimensional Laplacianfaces method is not only computationally more efficient but also more accurate than the one dimensional Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the Yale and the AR face databases. The experimental results indicate that the two dimensional Laplacianfaces method significantly outperforms the existing two dimensional Eigenfaces, the two dimensional Fisherfaces and the one dimensional Laplacianfaces methods under the various settings of experiment conditions.

AB - In this paper we propose the two dimensional Laplacianfaces method for face recognition. The new algorithm is developed based on the two techniques, i.e., locality preserved embedding and image based projection. The two dimensional Laplacianfaces method is not only computationally more efficient but also more accurate than the one dimensional Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the Yale and the AR face databases. The experimental results indicate that the two dimensional Laplacianfaces method significantly outperforms the existing two dimensional Eigenfaces, the two dimensional Fisherfaces and the one dimensional Laplacianfaces methods under the various settings of experiment conditions.

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M3 - Conference article published in proceeding or book

SN - 3540476938

SN - 9783540476931

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 852

EP - 861

BT - Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings

PB - Springer Verlag

T2 - 5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006

Y2 - 6 November 2006 through 8 November 2006

ER -

Two dimensional Laplacianfaces method for face recognition

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