Fusion-based age-group classification method using multiple two-dimensional feature extraction algorithms

Kazuya Ueki, Tetsunori Kobayashi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and twodimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.

Original languageEnglish
Pages (from-to)923-934
Number of pages12
JournalIEICE Transactions on Information and Systems
VolumeE90-D
Issue number6
DOIs
Publication statusPublished - 2007 Jun

Keywords

  • 2DLDA
  • 2DPCA
  • Age-group classification
  • Classification combination
  • Face recognition pattern recognition
  • Max rule
  • Min rule
  • Min-max normalization
  • Produc rule
  • Sum rule
  • Z-score

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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