Evaluation of the most appropriate Kernel function for the purpose of feature extraction in face recognition in video surveillance systems

Sepehr Damavandinejadmonfared; Sina Ashooritootkaboni; Taha Bahraminezhad Jooneghani

Evaluation of the most appropriate Kernel function for the purpose of feature extraction 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

It is believed that Kernel Principal Component Analysis (KPCA) merits the performance of Principal Component Analysis big time. KPCA is the nonlinear extend of Principal Component Analysis (PCA) meaning that in KPCA the input data is first mapped by using nonlinear Kernel and then PCA is performed on the mapped data. Kernel function has been categorized into four different categories such as Linear, Polynomial, Gaussian, and Sigmoid. In this work the performance of the mentioned methods on Surveillance Camera Faces database is observed to determine the significance of Polynomial KPCA compared to other types of that in terms of video surveillance recognition.

Original language	English
Title of host publication	Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Pages	1131-1134
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. / Evaluation of the most appropriate Kernel function for the purpose of feature extraction 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. 1131-1134

@inproceedings{b0adbae676fd401dac69f5301e2cc0a3,

title = "Evaluation of the most appropriate Kernel function for the purpose of feature extraction in face recognition in video surveillance systems",

abstract = "It is believed that Kernel Principal Component Analysis (KPCA) merits the performance of Principal Component Analysis big time. KPCA is the nonlinear extend of Principal Component Analysis (PCA) meaning that in KPCA the input data is first mapped by using nonlinear Kernel and then PCA is performed on the mapped data. Kernel function has been categorized into four different categories such as Linear, Polynomial, Gaussian, and Sigmoid. In this work the performance of the mentioned methods on Surveillance Camera Faces database is observed to determine the significance of Polynomial KPCA compared to other types of that in terms of video surveillance recognition.",

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 = "1131--1134",

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, Evaluation of the most appropriate Kernel function for the purpose of feature extraction 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. 1131-1134, 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012, Las Vegas, NV, United States, 16/07/12.

Evaluation of the most appropriate Kernel function for the purpose of feature extraction 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. 1131-1134.

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

TY - GEN

T1 - Evaluation of the most appropriate Kernel function for the purpose of feature extraction in face recognition in video surveillance systems

AU - Damavandinejadmonfared, Sepehr

AU - Ashooritootkaboni, Sina

AU - Jooneghani, Taha Bahraminezhad

PY - 2012

Y1 - 2012

N2 - It is believed that Kernel Principal Component Analysis (KPCA) merits the performance of Principal Component Analysis big time. KPCA is the nonlinear extend of Principal Component Analysis (PCA) meaning that in KPCA the input data is first mapped by using nonlinear Kernel and then PCA is performed on the mapped data. Kernel function has been categorized into four different categories such as Linear, Polynomial, Gaussian, and Sigmoid. In this work the performance of the mentioned methods on Surveillance Camera Faces database is observed to determine the significance of Polynomial KPCA compared to other types of that in terms of video surveillance recognition.

AB - It is believed that Kernel Principal Component Analysis (KPCA) merits the performance of Principal Component Analysis big time. KPCA is the nonlinear extend of Principal Component Analysis (PCA) meaning that in KPCA the input data is first mapped by using nonlinear Kernel and then PCA is performed on the mapped data. Kernel function has been categorized into four different categories such as Linear, Polynomial, Gaussian, and Sigmoid. In this work the performance of the mentioned methods on Surveillance Camera Faces database is observed to determine the significance of Polynomial KPCA compared to other types of that in terms of video surveillance recognition.

KW - Biometrics

KW - Face recognition

KW - Kernel Principal Component Analysis (KPCA)

KW - Pattern recognition

KW - Video surveillance systems

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M3 - Conference proceeding contribution

AN - SCOPUS:84873321366

SN - 9781601322258

VL - 2

SP - 1131

EP - 1134

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 -

Evaluation of the most appropriate Kernel function for the purpose of feature extraction in face recognition in video surveillance systems

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