Hand Gestures Detecting Using Radon and Fan Beam Projection Features

Abstract

Hand gesture recognition is one of the most effective ways of human interaction with a computer and it is also an important field in computer vision and machine learning. This theme enables many applications by allowing users to communicate the interfaces of different systems, without the need for additional hardware. That is, the primary goal of gesture recognition is to create systems in order to identify specific human gestures and use them to transmit information and signals or control various devices. There are more of research in the field of pattern recognition about gesture recognition, some of these researches focus on dimensionality reduction, others on the recognition model, on the type of feature selection and so on. In this paper, the static hand gesture is detected depending on the application of radon features and fan beam projection on hand images to calculate the projection at certain angles. The reason for adopting these techniques is to take advantage of their qualities in given features that are not related to the shape and size of the object. The decision tree model is used in the classification stage to discover five different hand gestures, and all the results are explained in the research below. With the adoption of these two projection methods in extracting the characteristics of the hand signal, the results showed high potential, where the highest success rate for classification was at the 90 theta, so the success rate with radon features was 88% and with beam projection was 91.8%.