Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition

Abstract

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.

Accepted by ICIP 2017

Cited by in corpus (5)

• CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition
• A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities
• An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition
• LE-HGR: A Lightweight and Efficient RGB-based Online Gesture Recognition Network for Embedded AR Devices
• Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN