Face detection using SVM trained in independent space

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages3674-3677
Number of pages4
StatePublished - 2004
EventProceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China
Duration: 26 Aug 200429 Aug 2004

Publication series

NameProceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume6

Conference

ConferenceProceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityShanghai
Period26/08/0429/08/04

Keywords

  • Face detection
  • ICA
  • PCA
  • SVM

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