A robust gender and age estimation under varying facial pose

Hironori Takimoto, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper presents a method for gender and age estimation which is robust for facial pose changing. We propose a feature point detection method which is 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 into the Retinal Sampling Method. In this method, feature points are detected based on 7 points corresponding to facial organ from face image. The reason why we used 7 points to basis of feature point detection is that facial organ is conspicuous in facial region, and it is comparatively easy to extract. As features which is robust for facial pose changing, a skin texture, a hue and a gabor jet are used for the gender and age estimation. For classification of gender and estimation of seriate age, we use a multi-layered neural network. Moreover, we examine the left-right symmetric property of the face concerning gender and age estimation by the proposed method.

Original languageEnglish
Pages (from-to)1022-1029+7
JournalIEEJ Transactions on Electronics, Information and Systems
Volume127
Issue number7
DOIs
Publication statusPublished - 2007 Jan 1

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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