# A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences

arXiv: https://arxiv.org/abs/2207.07619 · HTML: https://mlsanity.com/paper/2207.07619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [Sign Language Production: A Review](https://mlsanity.com/paper/2103.15910/) (2021)
- [Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search](https://mlsanity.com/paper/2002.08718/) (2020)
- [Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks](https://mlsanity.com/paper/2109.12946/) (2021)
- [All You Need In Sign Language Production](https://mlsanity.com/paper/2201.01609/) (2022)
- [ZS-SLR: Zero-Shot Sign Language Recognition from RGB-D Videos](https://mlsanity.com/paper/2108.10059/) (2021)
- [Multi-Modal Zero-Shot Sign Language Recognition](https://mlsanity.com/paper/2109.00796/) (2021)
- [Word separation in continuous sign language using isolated signs and post-processing](https://mlsanity.com/paper/2204.00923/) (2022)
- [REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition](https://mlsanity.com/paper/2105.06711/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Transformer Model for Boundary Detection in Continuous Sign Language](https://mlsanity.com/paper/2402.14720/) (2024)

## Abstract (arXiv)

Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences