A comprehensive leap motion database for hand gesture recognition

The touchless interaction has received considerable attention in recent years with benefit of removing the burden of physical contact. The recent introduction of novel acquisition devices, like the leap motion controller, allows obtaining a very informative description of the hand pose and motion that can be exploited for accurate gesture recognition. In this work, we present an interactive application with gestural hand control using leap motion for medical visualization, focusing on the satisfaction of the user as an important component in the composition of a new specific database. In this paper, we propose a 3D dynamic gesture recognition approach explicitly targeted to leap motion data. Spatial feature descriptors based on the positions of fingertips and palm center are extracted and fed into a support vector machine classifier in order to recognize the performed gestures. The experimental results show the effectiveness of the suggested approach in the recognition of the modeled gestures with a high accuracy rate of about 81%.