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Marker-Less Gesture and Facial Expression Based Affect Modeling

  • Sherlo Yvan Cantos
  • Jeriah Kjell Miranda
  • Melisa Renee Tiu
  • Mary Czarinelle Yeung
Part of the Proceedings in Information and Communications Technology book series (PICT, volume 7)

Abstract

Many affective Intelligent Tutoring Systems (ITSs) today use multi-modal approaches in recognizing student affect. These researches have had achieved promising results but they have their own limitations. Most are difficult to deploy because it requires special equipment/s which are disruptive to student activities. This work is an effort towards developing affective ITSs that are easy to deploy, scalable, accurate and inexpensive. This study uses a webcam and Microsoft Kinect to detect the facial expressions and body gestures of the students respectively. A corpus for 8 students were built and SVM PolyKernel, SVM PUK, SVM RBF, LogitBoost, and Multilayer Perceptron machine learning algorithms were applied to discover patterns over the facial expression and C4.5 was used for body gesture features. The body gestures and facial point distances data sets were used to build user-specific models. The range of f-Measure produced for fusion of gesture and face is 0.017 to 0.342.

Keywords

Facial Expression Emotion Recognition Neutral Position Confusion Matrix Intelligent Tutor System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Tokyo 2013

Authors and Affiliations

  • Sherlo Yvan Cantos
    • 1
  • Jeriah Kjell Miranda
    • 1
  • Melisa Renee Tiu
    • 1
  • Mary Czarinelle Yeung
    • 1
  1. 1.De La Salle University-ManilaManilaPhilippines

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