Pose invariant face recognition for video surveillance system using kernel principle component analysis

Sepehr Damavandinejadmonfared; Waled Hussein Al-Arashi; Shahrel Azmin Suandi

doi:10.1117/12.956494

Pose invariant face recognition for video surveillance system using kernel principle component analysis

Sepehr Damavandinejadmonfared^*, Waled Hussein Al-Arashi, Shahrel Azmin Suandi

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Kernel Entropy Component Analysis (KECA) is a newer method than Kernel Principle Component Analysis (KPCA) for data transformation and dimensionality reduction in case of face recognition. Although in almost all previous researches using KECA are shown to be more superior and more appropriate method compared to KPCA, here in this paper the significance of Kernel PCA in handling face pose in surveillance images is compared to KECA. Comparative analysis is made to signify the importance of Kernel Principle Component Analysis in terms of pose invariant face recognition in surveillance.

Original language	English
Title of host publication	Fourth International Conference on Digital Image Processing, ICDIP 2012
Pages	1-5
Number of pages	5
Volume	8334
DOIs	https://doi.org/10.1117/12.956494
Publication status	Published - 2012
Externally published	Yes
Event	4th International Conference on Digital Image Processing, ICDIP 2012 - Kuala Lumpur, Malaysia Duration: 7 Apr 2012 → 8 Apr 2012

Other

Other	4th International Conference on Digital Image Processing, ICDIP 2012
Country/Territory	Malaysia
City	Kuala Lumpur
Period	7/04/12 → 8/04/12

Keywords

face recognition
Kernel Entropy Component Analysis (KECA)
Kernel Principal Component Analysis (KPCA)

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10.1117/12.956494

Cite this

@inproceedings{b361c893b0e84546b7bc5b8f525d4db2,

title = "Pose invariant face recognition for video surveillance system using kernel principle component analysis",

abstract = "Kernel Entropy Component Analysis (KECA) is a newer method than Kernel Principle Component Analysis (KPCA) for data transformation and dimensionality reduction in case of face recognition. Although in almost all previous researches using KECA are shown to be more superior and more appropriate method compared to KPCA, here in this paper the significance of Kernel PCA in handling face pose in surveillance images is compared to KECA. Comparative analysis is made to signify the importance of Kernel Principle Component Analysis in terms of pose invariant face recognition in surveillance.",

keywords = "face recognition, Kernel Entropy Component Analysis (KECA), Kernel Principal Component Analysis (KPCA)",

author = "Sepehr Damavandinejadmonfared and Al-Arashi, {Waled Hussein} and Suandi, {Shahrel Azmin}",

year = "2012",

doi = "10.1117/12.956494",

language = "English",

isbn = "9780819489913",

volume = "8334",

pages = "1--5",

booktitle = "Fourth International Conference on Digital Image Processing, ICDIP 2012",

note = "4th International Conference on Digital Image Processing, ICDIP 2012 ; Conference date: 07-04-2012 Through 08-04-2012",

}

Damavandinejadmonfared, S, Al-Arashi, WH & Suandi, SA 2012, Pose invariant face recognition for video surveillance system using kernel principle component analysis. in Fourth International Conference on Digital Image Processing, ICDIP 2012. vol. 8334, 833439, pp. 1-5, 4th International Conference on Digital Image Processing, ICDIP 2012, Kuala Lumpur, Malaysia, 7/04/12. https://doi.org/10.1117/12.956494

Pose invariant face recognition for video surveillance system using kernel principle component analysis. / Damavandinejadmonfared, Sepehr; Al-Arashi, Waled Hussein; Suandi, Shahrel Azmin.
Fourth International Conference on Digital Image Processing, ICDIP 2012. Vol. 8334 2012. p. 1-5 833439.

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

TY - GEN

T1 - Pose invariant face recognition for video surveillance system using kernel principle component analysis

AU - Damavandinejadmonfared, Sepehr

AU - Al-Arashi, Waled Hussein

AU - Suandi, Shahrel Azmin

PY - 2012

Y1 - 2012

N2 - Kernel Entropy Component Analysis (KECA) is a newer method than Kernel Principle Component Analysis (KPCA) for data transformation and dimensionality reduction in case of face recognition. Although in almost all previous researches using KECA are shown to be more superior and more appropriate method compared to KPCA, here in this paper the significance of Kernel PCA in handling face pose in surveillance images is compared to KECA. Comparative analysis is made to signify the importance of Kernel Principle Component Analysis in terms of pose invariant face recognition in surveillance.

AB - Kernel Entropy Component Analysis (KECA) is a newer method than Kernel Principle Component Analysis (KPCA) for data transformation and dimensionality reduction in case of face recognition. Although in almost all previous researches using KECA are shown to be more superior and more appropriate method compared to KPCA, here in this paper the significance of Kernel PCA in handling face pose in surveillance images is compared to KECA. Comparative analysis is made to signify the importance of Kernel Principle Component Analysis in terms of pose invariant face recognition in surveillance.

KW - face recognition

KW - Kernel Entropy Component Analysis (KECA)

KW - Kernel Principal Component Analysis (KPCA)

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U2 - 10.1117/12.956494

DO - 10.1117/12.956494

M3 - Conference proceeding contribution

AN - SCOPUS:84874696007

SN - 9780819489913

VL - 8334

SP - 1

EP - 5

BT - Fourth International Conference on Digital Image Processing, ICDIP 2012

T2 - 4th International Conference on Digital Image Processing, ICDIP 2012

Y2 - 7 April 2012 through 8 April 2012

ER -

Pose invariant face recognition for video surveillance system using kernel principle component analysis

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Keywords

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