Face detection using classifiers cascade based on vector angle measure and multi-modal representation

F. Flitti, Amine Bermak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper deals with face detection in still gray level images which is the first step in many automatic systems like video surveillance, face recognition, and images data base management. We propose a new face detection method using a classifiers cascade, each of which is based on a vector angle similarity measure between the investigated window and the face and nonface representatives (centroids). The latter are obtained using a clustering algorithm based on the same measure within the current training data sets, namely the low confidence classified samples at the previous stage of the cascade. First experiment results on refereed face data test sets are very satisfactory.

Original languageEnglish
Title of host publication2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Proceedings
Pages539-542
Number of pages4
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE Workshop on Signal Processing Systems, SiPS 2007 - Shanghai, China
Duration: 17 Oct 200719 Oct 2007

Other

Other2007 IEEE Workshop on Signal Processing Systems, SiPS 2007
CountryChina
CityShanghai
Period17/10/0719/10/07

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Keywords

  • Classifiers cascade
  • Face detection
  • Low confidence decision based training
  • Vector angle

ASJC Scopus subject areas

  • Media Technology
  • Signal Processing

Cite this

Flitti, F., & Bermak, A. (2007). Face detection using classifiers cascade based on vector angle measure and multi-modal representation. In 2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Proceedings (pp. 539-542). [4387605] https://doi.org/10.1109/SIPS.2007.4387605