Facial Expression Recognition Using Features of Density Distributions between Facial Images

DOI 14 References
  • NAKAMURA Munehiro
    Graduate School of Natural Science and Technology, Kanazawa University
  • KAJIWARA Yusuke
    Graduate School of Natural Science and Technology, Kanazawa University
  • MURATA Hiroaki
    The Institute of Nature and Environmental Technology, Kanazawa University
  • KIMURA Haruhiko
    Graduate School of Natural Science and Technology, Kanazawa University

Bibliographic Information

Other Title
  • 顔画像間の濃度特徴を用いた表情認識

Description

This paper presents a method of discriminating human facial expressions using 5 pattern classifiers, where features based on density distributions are extracted from target regions set in facial feature points. Observing human facial expressions in videos, we could find that wrinkles appear in regions of correlated mimic muscles. The proposed method extracts the degree of similarity based on Zero-Mean Normalized Cross-Correlation as features from target areas where wrinkles often appear. And, 5 pattern classifiers, namely, Nearest Neighbor, Random Forests, Logistic Regression, Support Vector Machine, and Neural Network, are applied to discrimination of 6 basic facial expressions. The efficiency of the proposed method has been confirmed using public facial expression databases.

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Details 詳細情報について

  • CRID
    1390282680162189568
  • NII Article ID
    130002096886
  • DOI
    10.3156/jsoft.24.836
  • ISSN
    18817203
    13477986
  • Text Lang
    ja
  • Article Type
    journal article
  • Data Source
    • JaLC
    • Crossref
    • CiNii Articles
    • KAKEN
  • Abstract License Flag
    Disallowed

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