Hand gestures recognition classification

Abstract

The use of hand gestures for human-machine interaction offers an enticing alternative to bulky interface devices. The current study discusses the classification of gestures in real time and aims to create an algorithm capable of classifying gestural control commands accurately. For the classification of a gesture vocabulary of eight dynamic hand gestures, two separate classifiers were created. The established classifiers were: K-means + rule-based classifier and classifier of to test the accuracy of classification recognition in which a test set of 180 trajectories was categorized, an experiment was conducted. The accuracies obtained for the K-means and Learning classifier systems( LCS ) classifiers, respectively, are 90 and 94 percent.