Face Detection and Facial Component Extraction by Wavelet Decomposition and Support Vector Machines

Dihua Xi, Seong Whan Lee

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Quite recently the support vector machine (SVM) has shown a great potential in the area of automatic face detection. Generally the SVM based methods fall into two categories: component-based and whole face-based. However there exist some limitations to each category. In this paper we present a two-stage method using both SVM categories based on multiresolution wavelet decomposition (MWD). In the first stage, the whole face-based SVMs are used for coarse location of faces from small sub-images of low resolution. Then a set of component-based SVMs are applied to verify the extracted candidates in subsequent larger sub-images of higher resolutions. Experimental results show that this wavelet-SVM based method takes the advantage of the effectiveness of both categories of SVM-based methods and the computation efficiency.

Original languageEnglish
Pages (from-to)199-207
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
Publication statusPublished - 2003 Dec 1

Fingerprint

Wavelet decomposition
Face Detection
Wavelet Decomposition
Face recognition
Support vector machines
Support Vector Machine
Face
Multiresolution
Wavelets
High Resolution
Verify
Experimental Results

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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