Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition

Xianye Ben; Weixiao Meng; Shi An; Ze Wang

doi:10.1109/ChinaCom.2011.6158253

Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition

Xianye Ben^*
, Weixiao Meng
, Shi An
, Ze Wang

^*Corresponding author for this work

School of Transportation Science and Engineering, Harbin Institute of Technology
School of Electronics and Information Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, a novel algorithm for feature extraction - Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) is proposed. The improved SpC2DLPPCA algorithm over C2DLPPCA and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefits greatly to three points: (1) SpC2DLPPCA can overcome a failing that larger dimension matrix may bring about more consuming time on computing its eigenvalues and eigenvectors. (2) SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact the expression of features. Finally, experiments on the CASIA(B) gait database show that SpC2DLPPCA has higher recognition accuracies than C2DLPPCA and SpC2DPCA.

Original language	English
Title of host publication	Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011
Pages	747-752
Number of pages	6
DOIs	https://doi.org/10.1109/ChinaCom.2011.6158253
State	Published - 2011
Externally published	Yes
Event	2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011 - Harbin, China Duration: 17 Aug 2011 → 19 Aug 2011

Publication series

Name	Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011

Conference

Conference	2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011
Country/Territory	China
City	Harbin
Period	17/08/11 → 19/08/11

Keywords

Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA)
Two Dimensional Locality Preserving projections (2DLPP)
Two Dimensional Principal Component Analysis (2DPCA)
gait recognition

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10.1109/ChinaCom.2011.6158253

Cite this

Ben, X., Meng, W., An, S., & Wang, Z. (2011). Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition. In Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011 (pp. 747-752). Article 6158253 (Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011). https://doi.org/10.1109/ChinaCom.2011.6158253

Ben, Xianye ; Meng, Weixiao ; An, Shi et al. / Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition. Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011. 2011. pp. 747-752 (Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011).

@inproceedings{e5a9d1b02c2743259991931ade4ed82b,

title = "Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition",

abstract = "In this paper, a novel algorithm for feature extraction - Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) is proposed. The improved SpC2DLPPCA algorithm over C2DLPPCA and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefits greatly to three points: (1) SpC2DLPPCA can overcome a failing that larger dimension matrix may bring about more consuming time on computing its eigenvalues and eigenvectors. (2) SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact the expression of features. Finally, experiments on the CASIA(B) gait database show that SpC2DLPPCA has higher recognition accuracies than C2DLPPCA and SpC2DPCA.",

keywords = "Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA), Two Dimensional Locality Preserving projections (2DLPP), Two Dimensional Principal Component Analysis (2DPCA), gait recognition",

author = "Xianye Ben and Weixiao Meng and Shi An and Ze Wang",

year = "2011",

doi = "10.1109/ChinaCom.2011.6158253",

language = "英语",

isbn = "9781457701016",

series = "Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011",

pages = "747--752",

booktitle = "Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011",

note = "2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011 ; Conference date: 17-08-2011 Through 19-08-2011",

}

Ben, X, Meng, W , An, S & Wang, Z 2011, Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition. in Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011., 6158253, Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011, pp. 747-752, 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011, Harbin, China, 17/08/11. https://doi.org/10.1109/ChinaCom.2011.6158253

Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition. / Ben, Xianye; Meng, Weixiao ; An, Shi et al.
Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011. 2011. p. 747-752 6158253 (Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ben, Xianye

AU - Meng, Weixiao

AU - An, Shi

AU - Wang, Ze

PY - 2011

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N2 - In this paper, a novel algorithm for feature extraction - Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) is proposed. The improved SpC2DLPPCA algorithm over C2DLPPCA and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefits greatly to three points: (1) SpC2DLPPCA can overcome a failing that larger dimension matrix may bring about more consuming time on computing its eigenvalues and eigenvectors. (2) SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact the expression of features. Finally, experiments on the CASIA(B) gait database show that SpC2DLPPCA has higher recognition accuracies than C2DLPPCA and SpC2DPCA.

AB - In this paper, a novel algorithm for feature extraction - Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) is proposed. The improved SpC2DLPPCA algorithm over C2DLPPCA and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefits greatly to three points: (1) SpC2DLPPCA can overcome a failing that larger dimension matrix may bring about more consuming time on computing its eigenvalues and eigenvectors. (2) SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact the expression of features. Finally, experiments on the CASIA(B) gait database show that SpC2DLPPCA has higher recognition accuracies than C2DLPPCA and SpC2DPCA.

KW - Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA)

KW - Two Dimensional Locality Preserving projections (2DLPP)

KW - Two Dimensional Principal Component Analysis (2DPCA)

KW - gait recognition

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DO - 10.1109/ChinaCom.2011.6158253

M3 - 会议稿件

AN - SCOPUS:84863294164

SN - 9781457701016

T3 - Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011

SP - 747

EP - 752

BT - Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011

T2 - 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011

Y2 - 17 August 2011 through 19 August 2011

ER -

Ben X, Meng W , An S, Wang Z. Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition. In Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011. 2011. p. 747-752. 6158253. (Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011). doi: 10.1109/ChinaCom.2011.6158253

Subpattern complete two dimensional locality preserving principal component analysis and its application to gait recognition

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