正準相関分析と注視特性による顔表情画像からの感情の測定法  [in Japanese] A Method for Measuring Emotion of Facial Expression Images by Using CCA and Gazing Property  [in Japanese]

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Abstract

本論文は正準相関分析を用いて顔表情画像から感情の程度を測定する手法を提案する.ここでは,顔表情のグレースケール画像およびその表情・感情を主観評価した値(快-不快および覚醒-沈静)との2種類の変量間における正準相関を分析する.顔表情画像に対する人の視線の動きから求めた注視特性を,本測定法の加重関数として組み込み,その有効性を述べる.また,画像のサンプル数の不足から生じる非正則な共分散データの場合,その情報を効率的に分析するために,中間変数を導入した正準相関分析の分解手法を提案する.また,ガウスカーネルによる非線形カーネル正準相関分析の効果について,線形の正準相関分析と比較し,その有効性を示す.男性と女性に分けた顔表情画像データベースに対して Leave-One-Out法による数値実験を行った結果と動画像に対する測定実験を行った結果を示すことにより,本提案法の有効性を示す.

This paper proposes a method for measuring and estimating emotion level of a facial expression image by using canonical correlation analysis (CCA) and Kernel CCA (KCCA). According to well known circumplex model of emotion by J.A.Russel, we adopt a scheme of representing emotion by two coordinate axes of valence and arousal. Our CCA and KCCA analyze two groups of variables: 1) gray scale images of facial expressions, and 2) their subjectively evaluated values of emotion in the two coordinate axes. In order to reduce error or uncertainty of estimation for emotion level, several weighting functions for reading facial expression are assembled from human gazing property of eye tracking data. Due to insufficient number of training image samples, CCA method often gets into difficulty of singular covariance matrices. A solution for the difficulty is proposed in this paper by introducing intermediate variables via principal component analysis (PCA). Property of nonlinear KCCA method with Gaussian kernel is compared to that of CCA method. The method is applied to databases of still images and movie images of facial expressions. The experimental results show that the proposed method is effective in the sense that it can estimate the level of emotion from facial expression images with comparable estimation uncertainty to human observers.

Journal

  • Journal of Japan Society for Fuzzy Theory and Intelligent Informatics

    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 22(1), 52-64, 2010-02-15

    Japan Society for Fuzzy Theory and Intelligent Informatics

References:  19

Codes

  • NII Article ID (NAID)
    10025996206
  • NII NACSIS-CAT ID (NCID)
    AA1181479X
  • Text Lang
    JPN
  • Article Type
    ART
  • ISSN
    13477986
  • NDL Article ID
    10589514
  • NDL Call No.
    Z15-649
  • Data Source
    CJP  NDL  J-STAGE 
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