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MOTION FEATURE AUGMENTED RECURRENT NEURAL NETWORK FOR SKELETON-BASED DYNAMIC HAND GESTURE RECOGNITION

Citation Author(s):
Xinghao Chen, Hengkai Guo, Guijin Wang, Li Zhang
Submitted by:
Xinghao Chen
Last updated:
16 September 2017 - 11:39am
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Xinghao Chen
Paper Code:
2077
 

Dynamic hand gesture recognition has attracted increasing interests because of its importance for human computer interaction. In this paper, we propose a new motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. Finger motion features are extracted to describe finger movements and global motion features are utilized to represent the global movement of hand skeleton. These motion features are then fed into a bidirectional recurrent neural network (RNN) along with the skeleton sequence, which can augment the motion features for RNN and improve the classification performance. Experiments demonstrate that our proposed method is effective and outperforms start-of-the-art methods.

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