Gesture Recognition, 1st ed. 2017
The Springer Series on Challenges in Machine Learning Series

This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.

Gives readers a comprehensive analysis on gesture recognition, defining a new taxonomy for the field

Focusses on supervised machine learning methods for gesture recognition

Presents an open-source C++ library for real-time gesture recognition

Reviews recent research involving deep learning architectures in order to deal with gesture and action recognition problems

Includes supplementary material: sn.pub/extras

Includes supplementary material: sn.pub/extras