ArticleSensors (Basel, Switzerland)2019
MFA-Net: Motion Feature Augmented Network for Dynamic Hand Gesture Recognition from Skeletal Data.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Spatio-Temporal Transformer with Kolmogorov-Arnold Network for Skeleton-Based Hand Gesture Recognition.Sensors (Basel, Switzerland) · 2025Article
- CrackNet: A Hybrid Model for Crack Segmentation with Dynamic Loss Function.Sensors (Basel, Switzerland) · 2024Article
- Real-Time Monocular Skeleton-Based Hand Gesture Recognition Using 3D-Jointsformer.Sensors (Basel, Switzerland) · 2023Article
- HGR-ViT: Hand Gesture Recognition with Vision Transformer.Sensors (Basel, Switzerland) · 2023Article
- American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM.Sensors (Basel, Switzerland) · 2022Article
- A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition.PeerJ. Computer science · 2022Article
- Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks.Sensors (Basel, Switzerland) · 2020Article
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6 authors.
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Abstract
Dynamic hand gesture recognition has attracted increasing attention because of its importance for human⁻computer interaction. In this paper, we propose a novel motion feature augmented network (MFA-Net) for dynamic hand gesture recognition from skeletal data. MFA-Net exploits motion features of finger and global movements to augment features of deep network for gesture recognition. To describe finger articulated movements, finger motion features are extracted from the hand skeleton sequence via a variational autoencoder. Global motion features are utilized to represent the global movements of hand skeleton. These motion features along with the skeleton sequence are then fed into three branches of a recurrent neural network (RNN), which augment the motion features for RNN and improve the classification performance. The proposed MFA-Net is evaluated on two challenging skeleton-based dynamic hand gesture datasets, including DHG-14/28 dataset and SHREC'17 dataset. Experimental results demonstrate that our proposed method achieves comparable performance on DHG-14/28 dataset and better performance on SHREC'17 dataset when compared with start-of-the-art methods.
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