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Facial Expression Recognition for Domestic Service Robots

  • 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.

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Document Type:Conference Object
Author:Geovanny Giorgana, Paul G. Ploeger
Parent Title (English):Röfer, Mayer et al. (Eds.): RoboCup 2011: Robot Soccer World Cup XV
First Page:353
Last Page:364
Publication year:2012
Tag:Face normalization; One-vs-One multi-classification
AdaBoost; Error-correcting output codes; Facial expression recognition; Gabor features; Support Vector Machines
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2015/04/02