Face detection using SVM trained in independent space

Quan Xue Gao; Quan Pan; Hong Cai Zhang; Yong Mei Cheng; Qi Chuan Tian

Face detection using SVM trained in independent space

Quan Xue Gao, Quan Pan, Hong Cai Zhang, Yong Mei Cheng, Qi Chuan Tian

School of Automation

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

3 Scopus citations

Abstract

The classical face representation method, such as eigenface, extracts covariance based on low-order statistics feature of image. However, high-order information represents image details, which are necessary for pattern recognition. Hence, PCA is first used to reduce its dimension; then the Independent Component Analysis (ICA) is applied to further obtain independent feature vector instead of low-order statistics; finally support vector machine is used as a classifier that has demonstrated high generalization capabilities for face detection. The feasibility and correctness of this new face detection method are shown in CBCL Face Dataset.

Original language	English
Title of host publication	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages	3674-3677
Number of pages	4
State	Published - 2004
Event	Proceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: 26 Aug 2004 → 29 Aug 2004

Publication series

Name	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume	6

Conference

Conference	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Shanghai
Period	26/08/04 → 29/08/04

Keywords

Face detection
ICA
PCA
SVM

Cite this

@inproceedings{9f16f66b3369411f885a4c53e113372b,

title = "Face detection using SVM trained in independent space",

abstract = "The classical face representation method, such as eigenface, extracts covariance based on low-order statistics feature of image. However, high-order information represents image details, which are necessary for pattern recognition. Hence, PCA is first used to reduce its dimension; then the Independent Component Analysis (ICA) is applied to further obtain independent feature vector instead of low-order statistics; finally support vector machine is used as a classifier that has demonstrated high generalization capabilities for face detection. The feasibility and correctness of this new face detection method are shown in CBCL Face Dataset.",

keywords = "Face detection, ICA, PCA, SVM",

author = "Gao, {Quan Xue} and Quan Pan and Zhang, {Hong Cai} and Cheng, {Yong Mei} and Tian, {Qi Chuan}",

year = "2004",

language = "英语",

isbn = "0780384032",

series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

pages = "3674--3677",

booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

note = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

}

Gao, QX, Pan, Q, Zhang, HC, Cheng, YM & Tian, QC 2004, Face detection using SVM trained in independent space. in Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3674-3677, Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26/08/04.

Face detection using SVM trained in independent space. / Gao, Quan Xue; Pan, Quan; Zhang, Hong Cai et al.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics. 2004. p. 3674-3677 (Proceedings of 2004 International Conference on Machine Learning and Cybernetics; Vol. 6).

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

TY - GEN

T1 - Face detection using SVM trained in independent space

AU - Gao, Quan Xue

AU - Pan, Quan

AU - Zhang, Hong Cai

AU - Cheng, Yong Mei

AU - Tian, Qi Chuan

PY - 2004

Y1 - 2004

N2 - The classical face representation method, such as eigenface, extracts covariance based on low-order statistics feature of image. However, high-order information represents image details, which are necessary for pattern recognition. Hence, PCA is first used to reduce its dimension; then the Independent Component Analysis (ICA) is applied to further obtain independent feature vector instead of low-order statistics; finally support vector machine is used as a classifier that has demonstrated high generalization capabilities for face detection. The feasibility and correctness of this new face detection method are shown in CBCL Face Dataset.

AB - The classical face representation method, such as eigenface, extracts covariance based on low-order statistics feature of image. However, high-order information represents image details, which are necessary for pattern recognition. Hence, PCA is first used to reduce its dimension; then the Independent Component Analysis (ICA) is applied to further obtain independent feature vector instead of low-order statistics; finally support vector machine is used as a classifier that has demonstrated high generalization capabilities for face detection. The feasibility and correctness of this new face detection method are shown in CBCL Face Dataset.

KW - Face detection

KW - ICA

KW - PCA

KW - SVM

UR - http://www.scopus.com/inward/record.url?scp=6344239049&partnerID=8YFLogxK

M3 - 会议稿件

AN - SCOPUS:6344239049

SN - 0780384032

T3 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

SP - 3674

EP - 3677

BT - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

T2 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

Face detection using SVM trained in independent space

Abstract

Publication series

Conference

Keywords

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