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Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network

  • Noriki NishidaEmail author
  • Hideki Nakayama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9431)

Abstract

In this paper, we present a novel method for multimodal gesture recognition based on neural networks. Our multi-stream recurrent neural network (MRNN) is a completely data-driven model that can be trained from end to end without domain-specific hand engineering. The MRNN extends recurrent neural networks with Long Short-Term Memory cells (LSTM-RNNs) that facilitate the handling of variable-length gestures. We propose a recurrent approach for fusing multiple temporal modalities using multiple streams of LSTM-RNNs. In addition, we propose alternative fusion architectures and empirically evaluate the performance and robustness of these fusion strategies. Experimental results demonstrate that the proposed MRNN outperforms other state-of-the-art methods in the Sheffield Kinect Gesture (SKIG) dataset, and has significantly high robustness to noisy inputs.

Keywords

Multimodal gesture recognition Recurrent neural networks Long short-term memory Convolutional neural networks 

Notes

Acknowledgments

This work was supported by JST CREST, JSPS KAKENHI Grant Number 26730085. We would like to thank the three anonymous reviewers for their valuable feedback on this work.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Machine Perception Group, Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan

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