Incremental learning of hand gestures based on submovement sharing

Ryo Kawahata, Yanrung Wang, Atsushi Shimada, Takayoshi Yamashita, Rin Ichiro Taniguchi

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


This paper presents an incremental learning method for hand gesture recognition that learns the individual movements in each gesture of a user. To recognize the movement, we use a subunit-based dynamic time warping method, which treats a hand movement as a sequence of ubmovements. In our method, each hand movement is decomposed into submovements and the arrangement of submovements is reflected in the training sample database. Experimental results from the lassification of ten gestures demonstrate that our method can improve the recognition rate compared with a method without incremental learning. In addition, the experimental results show that incremental learning of a single class of gestures can improve the recognition rate of multi-class gestures using our method.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings
EditorsMohamed S. Kamel, Aurélio Campilho, Aurélio Campilho
PublisherSpringer Verlag
Number of pages8
ISBN (Electronic)9783319117546
Publication statusPublished - 2014
Event11th International Conference on Image Analysis and Recognition, ICIAR 2014 - Vilamoura, Portugal
Duration: Oct 22 2014Oct 24 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other11th International Conference on Image Analysis and Recognition, ICIAR 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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