Class-distance-based discriminant analysis and its application to supervised automatic age estimation

Tetsuji Ogawa*, Kazuya Ueki, Tetsunori Kobayashi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We propose a novel method of supervised feature projection called class-distance-based discriminant analysis (CDDA), which is suitable for automatic age estimation (AAE) from facial images. Most methods of supervised feature projection, e.g., Fisher discriminant analysis (FDA) and local Fisher discriminant analysis (LFDA), focus on determining whether two samples belong to the same class (i.e., the same age in AAE) or not. Even if an estimated age is not consistent with the correct age in AAE systems, i.e., the AAE system induces error, smaller errors are better. To treat such characteristics in AAE, CDDA determines between-class separability according to the class distance (i.e., difference in ages); two samples with similar ages are imposed to be close and those with spaced ages are imposed to be far apart. Furthermore, we propose an extension of CDDA called local CDDA (LCDDA), which aims at handling multimodality in samples. Experimental results revealed that CDDA and LCDDA could extract more discriminative features than FDA and LFDA.

Original languageEnglish
Pages (from-to)1683-1689
Number of pages7
JournalIEICE Transactions on Information and Systems
Issue number8
Publication statusPublished - 2011 Aug


  • Automatic age estimation
  • CDDA
  • Dimensionality reduction
  • FDA
  • LFDA

ASJC Scopus subject areas

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


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