Robust gender and age estimation under varying facial pose

Hironori Takimoto, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


This paper presents a method for gender and age estimation which is robust to changing facial pose. We propose a feature point detection method, called the adapted retinal sampling method (ARSM), and a feature extraction method. A basic concept of the ARSM is to add knowledge about the facial structure to the retinal sampling method. In this method, feature points are detected on the basis of seven points corresponding to facial organs from a facial image. The reason why we used seven points as the basis of feature point detection is that facial organs are conspicuous in the facial region, and are comparatively easy to extract. As features robust to changing facial pose, skin texture, hue, and the Gabor jet are used for gender and age estimation. For classification of gender and estimation of age, we use a multilayered neural network. We also examine the left-right symmetry of faces in connection with gender and age estimation by the proposed method.

Original languageEnglish
Pages (from-to)32-40
Number of pages9
JournalElectronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi)
Issue number7
Publication statusPublished - 2008
Externally publishedYes


  • ARSM
  • Facial image processing
  • Gender and age estimation
  • Neural network

ASJC Scopus subject areas

  • General Physics and Astronomy
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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