A Decomposition Approach for Complex Gesture Recognition Using DTW and Prefix Tree

Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Gestures are effective tools for expressing emotions and conveying information to the environment. Sequence matching and machine-learning based algorithm are two main methods to recognize continuous gestures. Machine-learning based recognition systems are not flexible to new gestures because the models have to be trained again. On the other hand, the computational time that matching methods required increases with the complexity and the class of the gestures. In this work, we propose a decomposition approach for complex gesture recognition utilizing DTW and prefix tree. This system can recognize 100 gestures with an accuracy of 97.38%.
Original languageEnglish (US)
Title of host publication2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
PublisherIEEE
Pages876-877
Number of pages2
ISBN (Print)9781728113777
DOIs
StatePublished - Aug 15 2019

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