XKin: an open source framework for hand pose and gesture recognition using kinect

Abstract

This work targets real-time recognition of both static hand-poses and dynamic hand-gestures in a unified open-source framework. The developed solution enables natural and intuitive hand-pose recognition of American Sign Language (ASL), extending the recognition to ambiguous letters not challenged by previous work. While hand-pose recognition exploits techniques working on depth information using texture-based descriptors, gesture recognition evaluates hand trajectories in the depth stream using angular features and hidden Markov models (HMM). Although classifiers come already trained on ASL alphabet and 16 uni-stroke dynamic gestures, users are able to extend these default sets by adding their personalized poses and gestures. The accuracy and robustness of the recognition system have been evaluated using a publicly available database and across many users. The XKin open project is available online (Pedersoli, XKin libraries. https://github.com/fpeder/XKin, 2013) under FreeBSD License for researchers in human-machine interaction