Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems

Sepehr Damavandinejadmonfared; Sina Ashooritootkaboni; Taha Bahraminezhad Jooneghani

Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems

Sepehr Damavandinejadmonfared^*, Sina Ashooritootkaboni, Taha Bahraminezhad Jooneghani

^*Corresponding author for this work

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

Abstract

Kernel Entropy Component Analysis (KECA) is one of the latest improvements on Principal Component Analysis (PCA) and also Kernel Principal Component Analysis (KPCA). As KECA is actually an extend of KPCA, different types of KPCA can be used in this method. In this paper the performance of four different types of Kernel Entropy Component Analysis (Linear, Polynomial, Gaussian, and Sigmoid) on Surveillance Camera Face database and also Head Pose Image database is observed in order to prove that Linear Kernel Entropy Component Analysis is the most appropriate method in terms of video surveillance.

Original language	English
Title of host publication	Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Pages	1163-1166
Number of pages	4
Volume	2
Publication status	Published - 2012
Externally published	Yes
Event	2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012 - Las Vegas, NV, United States Duration: 16 Jul 2012 → 19 Jul 2012

Other

Other	2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Country/Territory	United States
City	Las Vegas, NV
Period	16/07/12 → 19/07/12

Keywords

Biometrics
Face recognition
Kernel Principal Component Analysis (KPCA)
Pattern recognition
Video surveillance systems

Cite this

Damavandinejadmonfared, Sepehr ; Ashooritootkaboni, Sina ; Jooneghani, Taha Bahraminezhad. / Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems. Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012. Vol. 2 2012. pp. 1163-1166

@inproceedings{46368698ba994bfea0f8c1e2295865cb,

title = "Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems",

abstract = "Kernel Entropy Component Analysis (KECA) is one of the latest improvements on Principal Component Analysis (PCA) and also Kernel Principal Component Analysis (KPCA). As KECA is actually an extend of KPCA, different types of KPCA can be used in this method. In this paper the performance of four different types of Kernel Entropy Component Analysis (Linear, Polynomial, Gaussian, and Sigmoid) on Surveillance Camera Face database and also Head Pose Image database is observed in order to prove that Linear Kernel Entropy Component Analysis is the most appropriate method in terms of video surveillance.",

keywords = "Biometrics, Face recognition, Kernel Principal Component Analysis (KPCA), Pattern recognition, Video surveillance systems",

author = "Sepehr Damavandinejadmonfared and Sina Ashooritootkaboni and Jooneghani, {Taha Bahraminezhad}",

year = "2012",

language = "English",

isbn = "9781601322258",

volume = "2",

pages = "1163--1166",

booktitle = "Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012",

note = "2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012 ; Conference date: 16-07-2012 Through 19-07-2012",

}

Damavandinejadmonfared, S, Ashooritootkaboni, S & Jooneghani, TB 2012, Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems. in Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012. vol. 2, pp. 1163-1166, 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012, Las Vegas, NV, United States, 16/07/12.

Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems. / Damavandinejadmonfared, Sepehr; Ashooritootkaboni, Sina; Jooneghani, Taha Bahraminezhad.

Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012. Vol. 2 2012. p. 1163-1166.

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

TY - GEN

T1 - Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems

AU - Damavandinejadmonfared, Sepehr

AU - Ashooritootkaboni, Sina

AU - Jooneghani, Taha Bahraminezhad

PY - 2012

Y1 - 2012

N2 - Kernel Entropy Component Analysis (KECA) is one of the latest improvements on Principal Component Analysis (PCA) and also Kernel Principal Component Analysis (KPCA). As KECA is actually an extend of KPCA, different types of KPCA can be used in this method. In this paper the performance of four different types of Kernel Entropy Component Analysis (Linear, Polynomial, Gaussian, and Sigmoid) on Surveillance Camera Face database and also Head Pose Image database is observed in order to prove that Linear Kernel Entropy Component Analysis is the most appropriate method in terms of video surveillance.

AB - Kernel Entropy Component Analysis (KECA) is one of the latest improvements on Principal Component Analysis (PCA) and also Kernel Principal Component Analysis (KPCA). As KECA is actually an extend of KPCA, different types of KPCA can be used in this method. In this paper the performance of four different types of Kernel Entropy Component Analysis (Linear, Polynomial, Gaussian, and Sigmoid) on Surveillance Camera Face database and also Head Pose Image database is observed in order to prove that Linear Kernel Entropy Component Analysis is the most appropriate method in terms of video surveillance.

KW - Biometrics

KW - Face recognition

KW - Kernel Principal Component Analysis (KPCA)

KW - Pattern recognition

KW - Video surveillance systems

UR - http://www.scopus.com/inward/record.url?scp=84873314582&partnerID=8YFLogxK

M3 - Conference proceeding contribution

AN - SCOPUS:84873314582

SN - 9781601322258

VL - 2

SP - 1163

EP - 1166

BT - Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012

T2 - 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012

Y2 - 16 July 2012 through 19 July 2012

ER -

Using linear Kernel entropy component analysis as a feature extraction method in face recognition in video surveillance systems

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