An Ensemble Classifier Based on Three-Way Decisions for Social Touch Gesture Recognition

Abstract

Social 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. One of the application is the robot therapy that has great social significance. In this paper, an ensemble classifier based on threeway decisions is proposed to recognize touch gestures. Firstly, features are extracted from on six perspectives and four classifiers are constructed on different scales with different pre-processing methods. . Then an ensemble classifier is used to combine the four classifiers to classify the gestures. The proposed method is tested on the public Corpus of Social Touch (Cost) dataset. The experiments results not only verify the validity of our method but also show the better accuracy of our ensemble classifier.