Facial Expression Recognition for Domestic Service Robots

  • Geovanny Giorgana
  • Paul G. Ploeger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)


We present a system to automatically recognize facial expressions from static images. Our approach consists of extracting particular Gabor features from normalized face images and mapping them into three of the six basic emotions: joy, surprise and sadness, plus neutrality. Selection of the Gabor features is performed via the AdaBoost algorithm. We evaluated two learning machines (AdaBoost and Support Vector Machines), two multi-classification strategies (Error-Correcting Output Codes and One-vs-One) and two face image sizes (48 x 48 and 96 x 96). Images of the Cohn-Kanade AU-Coded Facial Expression Database were used as test bed for our research. Best results (87.14% recognition rate) were obtained using Support Vector Machines in combination with Error-Correcting Output Codes and normalized face images of 96 x 96.


Facial expression recognition Gabor features AdaBoost Support Vector Machines Error-correcting output codes One-vs-One multi-classification Face normalization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geovanny Giorgana
    • 1
  • Paul G. Ploeger
    • 1
  1. 1.Bonn-Rhein-Sieg University of Applied SciencesSankt AugustinGermany

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