A Robust Gender and Age Estimation under Varying Facial Pose

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  • Takimoto Hironori
    Dept. of Control Engineering, Sasebo National College of Technology
  • Mitsukura Yasue
    Graduate School of Bio-Applications and System Enginnering, Tokyo University of Agriculture and Technology
  • Fukumi Minoru
    Dept. of Information Science & Intteligent Syst. Faculty of Eng. University of Tokushima
  • Akamatsu Norio
    Dept. of Information Science & Intteligent Syst. Faculty of Eng. University of Tokushima

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Other Title
  • 姿勢変動に影響されない顔画像からの性別年齢推定
  • シセイ ヘンドウ ニ エイキョウサレナイ カオ ガゾウ カラノ セイベツ ネンレイ スイテイ
  • Robust gender and age estimation under varying facial pose

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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.

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