Classifier Fusion for Gesture Recognition using a Kinect Sensor

摘要

Gesture recognition is becoming a more and more popular research topic since it can be applied to lots of areas, such as vision-based interface, communication and interaction. In this paper, experiments are implemented to verify the potential to improve vision based gesture recognition performance using multiple classifiers. The proposed approach implements gesture recognition which combines decisions from a Dynamic Time Warping (DTW) based classifier and a Hidden Markov Model (HMM) based classifier. Both of these two classifiers share the same features extracted from the human skeleton model which is generated from a Kinect sensor. Then fusion rules are designed to make a global decision. The experiment results indicate that with the proposed fusion methods, the performance can be improved compared with either classifier.