An ensemble classifier based on three-way decisions for social touch gesture recognition

Abstract

Touch is an important form of social interaction. In Human Robot Interaction (HRI), touch can provide additional information to other modalities, such as audio, visual. In this paper, an ensemble classifier based on three-way decisions is proposed to recognize touch gestures. Firstly, features are extracted from six perspectives and four classifiers are constructed on different scales with different preprocessing methods. Then an ensemble classifier is used to combine the four classifiers to classify touch gestures. Our method is tested on the public Corpus of Social Touch (CoST) dataset. The experiment results not only verify the validity of our method but also show a better performance of our ensemble classifier.