# Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion

arXiv: https://arxiv.org/abs/1901.04622 · HTML: https://mlsanity.com/paper/1901.04622/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Multimodal Alignment and Fusion: A Survey](https://mlsanity.com/paper/2411.17040/) (2024)
- [PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points](https://mlsanity.com/paper/2210.11035/) (2022)
- [End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling](https://mlsanity.com/paper/2407.15047/) (2024)
- [Deep Unsupervised Key Frame Extraction for Efficient Video Classification](https://mlsanity.com/paper/2211.06742/) (2022)
- [Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks](https://mlsanity.com/paper/2205.06980/) (2022)

## Abstract (arXiv)

Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface. Although great progress has been made recently, fast and robust hand gesture recognition remains an open problem, since the existing methods have not well balanced the performance and the efficiency simultaneously. To bridge it, this work combines image entropy and density clustering to exploit the key frames from hand gesture video for further feature extraction, which can improve the efficiency of recognition. Moreover, a feature fusion strategy is also proposed to further improve feature representation, which elevates the performance of recognition. To validate our approach in a "wild" environment, we also introduce two new datasets called HandGesture and Action3D datasets. Experiments consistently demonstrate that our strategy achieves competitive results on Northwestern University, Cambridge, HandGesture and Action3D hand gesture datasets. Our code and datasets will release at https://github.com/Ha0Tang/HandGestureRecognition.