Compositional and Hierarchical Semantic Frameworks for Hand Gesture Recognition

Abstract

This chapter presents some new approaches in hand gesture recognition from monocular and from 3D images. After introducing the main trends from literature, the chapter addresses the hand gesture recognition problem in a compositional framework. The ability of compositional methods to capture and extract semantic information from selected salient parts of the images is demonstrated for the hand gesture application. Performances on images with static hand gestures executed on uniform backgrounds rival the state of the art, while living a lot of room for further optimization and improvement. The second work reported in this chapter is focused on using multiple cues to overcome difficult problems arising when dynamic gestures are executed in front of heterogeneous backgrounds, with camouflage and sudden illumination changes. Ideas from robust estimation are integrated in the proposed approach. Finally, some preliminary results of hand gesture recognition obtained with images generated by 3D time of flight image sensors, presumed to become prevalent in the near future, are presented.