On-line signature verification based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis)

Bin Li; Kuanquan Wang; Dapeng Zhang

On-line signature verification based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis)

Bin Li, Kuanquan Wang, Dapeng Zhang

Research output: Journal article publication › Journal article › Academic research › peer-review

Abstract

On-line signature verification is still an active topic in the field of biometrics. This paper proposes a novel method based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). Different from the application of PCA in other fields, both principal and minor components are used to signature verification, and MC plays a very important role. Comparing with DTW and the discriminance of Euclidean distance, the method based on PCA and MCA is better. With 1215 signatures contributed by 81 signers of which numbers of reference signatures, genuine signatures and forgeries (skilled) are 5 respectively, the EER is about 5%.

Original language	English
Pages (from-to)	540-546
Number of pages	7
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3072
Publication status	Published - 1 Dec 2004

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Cite this

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title = "On-line signature verification based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis)",

abstract = "On-line signature verification is still an active topic in the field of biometrics. This paper proposes a novel method based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). Different from the application of PCA in other fields, both principal and minor components are used to signature verification, and MC plays a very important role. Comparing with DTW and the discriminance of Euclidean distance, the method based on PCA and MCA is better. With 1215 signatures contributed by 81 signers of which numbers of reference signatures, genuine signatures and forgeries (skilled) are 5 respectively, the EER is about 5%.",

author = "Bin Li and Kuanquan Wang and Dapeng Zhang",

year = "2004",

month = dec,

day = "1",

language = "English",

volume = "3072",

pages = "540--546",

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

issn = "0302-9743",

publisher = "Springer Verlag",

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On-line signature verification based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). / Li, Bin; Wang, Kuanquan; Zhang, Dapeng.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3072, 01.12.2004, p. 540-546.

Research output: Journal article publication › Journal article › Academic research › peer-review

TY - JOUR

T1 - On-line signature verification based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis)

AU - Li, Bin

AU - Wang, Kuanquan

AU - Zhang, Dapeng

PY - 2004/12/1

Y1 - 2004/12/1

N2 - On-line signature verification is still an active topic in the field of biometrics. This paper proposes a novel method based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). Different from the application of PCA in other fields, both principal and minor components are used to signature verification, and MC plays a very important role. Comparing with DTW and the discriminance of Euclidean distance, the method based on PCA and MCA is better. With 1215 signatures contributed by 81 signers of which numbers of reference signatures, genuine signatures and forgeries (skilled) are 5 respectively, the EER is about 5%.

AB - On-line signature verification is still an active topic in the field of biometrics. This paper proposes a novel method based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). Different from the application of PCA in other fields, both principal and minor components are used to signature verification, and MC plays a very important role. Comparing with DTW and the discriminance of Euclidean distance, the method based on PCA and MCA is better. With 1215 signatures contributed by 81 signers of which numbers of reference signatures, genuine signatures and forgeries (skilled) are 5 respectively, the EER is about 5%.

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M3 - Journal article

SN - 0302-9743

VL - 3072

SP - 540

EP - 546

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

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

ER -

On-line signature verification based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis)

Abstract

ASJC Scopus subject areas

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