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Facial Expression Recognition from Still Images

  • Bilge Süheyla Akkoca GazioğluEmail author
  • Muhittin Gökmen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10284)

Abstract

With the development of technology, Facial Expression Recognition (FER) become one of the important research areas in Human Computer Interaction. Changes in the movement of some muscles in face create the facial expressions. By defining these changes, facial expressions can be recognized. In this study, a cascaded structure consists of Local Zernike Moments (LZM), Local XOR Patterns (LXP) and Global Zernike Moments (GZM) methods is proposed for the FER problem. The generally used database is the Extended Chon - Kanade (CK +) in FER problems. The database consists of image sequences of 327 expressions of 118 people. Most FER system includes recognition of 7 classes of emotions happiness, sadness, surprise, anger, disgust, fear and contempt, and we use Library of Support Vector Machines (LIBSVM) classifier for multi class classification with the leave one out cross-validation method. Our overall system performance is measured as 90.34% for FER.

Keywords

Facial Expression Recognition Local Zernike Moments Local XOR Patterns Global Zernike Moments 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bilge Süheyla Akkoca Gazioğlu
    • 1
    Email author
  • Muhittin Gökmen
    • 2
  1. 1.Istanbul Technical UniversityIstanbulTurkey
  2. 2.MEF UniversityIstanbulTurkey

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