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	Japan
City	Kobe
Period	6/11/06 → 8/11/06

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

More information

Scopus Link

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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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

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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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