Essence of 2DPCA and modification method for face recognition

Jun Hou; Quan Xue Gao; Quan Pan; Hong Cai Zhang

doi:10.1109/ICMLC.2006.258473

Essence of 2DPCA and modification method for face recognition

Jun Hou, Quan Xue Gao, Quan Pan, Hong Cai Zhang

School of Automation

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

2 Scopus citations

Abstract

In this paper, the method of 2DPCA is analyzed and its nature is revealed, i.e., 2DPCA is equivalent to view rows of face images as training samples that constitute row training sets and then use PCA for feature extraction. We also have proved that principal component vectors extracted by 2DPCA contain redundancy in theory. Based on this result, this paper presents a new image feature extraction method. The proposed method provides a sequentially optimal image compression mechanism. Finally, the effectiveness of the proposed algorithm is verified using the ORL database.

Original language	English
Title of host publication	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages	3351-3353
Number of pages	3
DOIs	https://doi.org/10.1109/ICMLC.2006.258473
State	Published - 2006
Event	2006 International Conference on Machine Learning and Cybernetics - Dalian, China Duration: 13 Aug 2006 → 16 Aug 2006

Publication series

Name	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Volume	2006

Conference

Conference	2006 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Dalian
Period	13/08/06 → 16/08/06

Keywords

2DPCA
Face recognition
Principal component analysis (PCA)

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10.1109/ICMLC.2006.258473

Cite this

Hou, J., Gao, Q. X., Pan, Q., & Zhang, H. C. (2006). Essence of 2DPCA and modification method for face recognition. In Proceedings of the 2006 International Conference on Machine Learning and Cybernetics (pp. 3351-3353). Article 4028646 (Proceedings of the 2006 International Conference on Machine Learning and Cybernetics; Vol. 2006). https://doi.org/10.1109/ICMLC.2006.258473

@inproceedings{f062a06f988c4ca292b6b20a601dd2a0,

title = "Essence of 2DPCA and modification method for face recognition",

abstract = "In this paper, the method of 2DPCA is analyzed and its nature is revealed, i.e., 2DPCA is equivalent to view rows of face images as training samples that constitute row training sets and then use PCA for feature extraction. We also have proved that principal component vectors extracted by 2DPCA contain redundancy in theory. Based on this result, this paper presents a new image feature extraction method. The proposed method provides a sequentially optimal image compression mechanism. Finally, the effectiveness of the proposed algorithm is verified using the ORL database.",

keywords = "2DPCA, Face recognition, Principal component analysis (PCA)",

author = "Jun Hou and Gao, {Quan Xue} and Quan Pan and Zhang, {Hong Cai}",

year = "2006",

doi = "10.1109/ICMLC.2006.258473",

language = "英语",

isbn = "1424400619",

series = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",

pages = "3351--3353",

booktitle = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",

note = "2006 International Conference on Machine Learning and Cybernetics ; Conference date: 13-08-2006 Through 16-08-2006",

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Hou, J, Gao, QX, Pan, Q & Zhang, HC 2006, Essence of 2DPCA and modification method for face recognition. in Proceedings of the 2006 International Conference on Machine Learning and Cybernetics., 4028646, Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, vol. 2006, pp. 3351-3353, 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13/08/06. https://doi.org/10.1109/ICMLC.2006.258473

Essence of 2DPCA and modification method for face recognition. / Hou, Jun; Gao, Quan Xue; Pan, Quan et al.
Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. 2006. p. 3351-3353 4028646 (Proceedings of the 2006 International Conference on Machine Learning and Cybernetics; Vol. 2006).

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

TY - GEN

T1 - Essence of 2DPCA and modification method for face recognition

AU - Hou, Jun

AU - Gao, Quan Xue

AU - Pan, Quan

AU - Zhang, Hong Cai

PY - 2006

Y1 - 2006

N2 - In this paper, the method of 2DPCA is analyzed and its nature is revealed, i.e., 2DPCA is equivalent to view rows of face images as training samples that constitute row training sets and then use PCA for feature extraction. We also have proved that principal component vectors extracted by 2DPCA contain redundancy in theory. Based on this result, this paper presents a new image feature extraction method. The proposed method provides a sequentially optimal image compression mechanism. Finally, the effectiveness of the proposed algorithm is verified using the ORL database.

AB - In this paper, the method of 2DPCA is analyzed and its nature is revealed, i.e., 2DPCA is equivalent to view rows of face images as training samples that constitute row training sets and then use PCA for feature extraction. We also have proved that principal component vectors extracted by 2DPCA contain redundancy in theory. Based on this result, this paper presents a new image feature extraction method. The proposed method provides a sequentially optimal image compression mechanism. Finally, the effectiveness of the proposed algorithm is verified using the ORL database.

KW - 2DPCA

KW - Face recognition

KW - Principal component analysis (PCA)

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SN - 1424400619

SN - 9781424400614

T3 - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics

SP - 3351

EP - 3353

BT - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics

T2 - 2006 International Conference on Machine Learning and Cybernetics

Y2 - 13 August 2006 through 16 August 2006

ER -

Essence of 2DPCA and modification method for face recognition

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