Gender classification based on integration of multiple classifiers using various features of facial and neck images

Kazuya Ueki, Tetsunori Kobayashi

Research output: Contribution to journalArticle

Abstract

To reduce the rate of error in gender classification, we propose the use of an integration framework that uses conventional facial images along with neck images. First, images are separated into facial and neck regions, and features are extracted from monochrome, color, and edge images of both regions. Second, we use Support Vector Machines (SVMs) to classify the gender of each individual feature. Finally, we reclassify the gender by considering the six types of distances from the optimal separating hyperplane as a 6-dimensional vector. Experimental results show a 28.4% relative reduction in error over the performance baseline of the monochrome facial image approach, which until now had been considered to have the most accurate performance.

Original languageEnglish
Pages (from-to)1803-1809
Number of pages7
JournalKyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
Volume61
Issue number12
DOIs
Publication statusPublished - 2007 Dec

Keywords

  • Feature extraction
  • Gender classification
  • Image processing
  • Pattern recognition
  • SVM

ASJC Scopus subject areas

  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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