Geometry-based static hand gesture recognition using support vector machine

Static hand gesture recognition plays an important role in developing a system for human-computer interaction. Besides, such systems can be also used by the deaf community in order to convey information through gestures instead of words. A vision-based processing of hand gesture recognition consists of three main stages: preprocessing, feature extraction and identification. In this paper, the first stage involves following two sub-stages: segmentation which locates hand using color information and extracts its silhouette; separation that separates arm, the part with less information, based on geometrical properties. In the second stage, features which extracted from hand-without-arm are general (ratio of width to height, wrist angle and number of fingers) and detailed (calculated based on fingertips and cross sections) characteristics. Finally, support vector machine model with "max-wins" voting strategy is used to classify the hand gestures. The experiment is conducted on color image dataset of Polish Ministry of Science and Higher Education, with 89.5% classification accuracy.