Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: survey. Springer Science+Business Media Dordrecht (2012)
Okada, S., Hasegawa, O.: Motion recognition based on dynamic-time warping method with self-organizing incremental neural network. In: The 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
Elmezain, M., Al-Hamadi, A., Michaelis, B.: Real-time capable system for hand gesture recognition using hidden Markov models in stereo color image sequences. Journal of WSCG, 65–72 (2008)
Wang, Y., Shimada, A., Yamasita, T., Taniguchi, R.: A subunit-based dynamic time warping approach for hand movement recognition. In: Petrosino, A. (ed.) ICIAP 2013, Part I. LNCS, vol. 8156, pp. 672–681. Springer, Heidelberg (2013)
Roussos, A., Theodorakis, S., Pitsikalis, V., Maragos, P.: Hand tracking and affine shape-appearance handshape sub-units in continuous sign language recognition. In: Kutulakos, K.N. (ed.) ECCV 2010 Workshops, Part I. LNCS, vol. 6553, pp. 258–272. Springer, Heidelberg (2012)
Bauer, B., Kraiss, K.-F.: Towards an automatic sign language recognition system using subunits. In: Wachsmuth, I., Sowa, T. (eds.) GW 2001. LNCS (LNAI), vol. 2298, pp. 64–75. Springer, Heidelberg (2002)
Giraud-Carrier, C.: A note on the utility of incremental learning. AI Communications 13(4), 215–223 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kawahata, R., Wang, Y., Shimada, A., Yamashita, T., Taniguchi, Ri. (2014). Incremental Learning of Hand Gestures Based on Submovement Sharing. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-11755-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11754-6
Online ISBN: 978-3-319-11755-3
eBook Packages: Computer ScienceComputer Science (R0)