# A novel shape matching descriptor for real-time hand gesture recognition

arXiv: https://arxiv.org/abs/2101.03923 · HTML: https://mlsanity.com/paper/2101.03923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 56 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [3D Hand Shape and Pose from Images in the Wild](https://mlsanity.com/paper/1902.03451/) (2019)
- [Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild](https://mlsanity.com/paper/2004.01946/) (2020)
- [Hashing for Similarity Search: A Survey](https://mlsanity.com/paper/1408.2927/) (2014)
- [Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient](https://mlsanity.com/paper/1408.1759/) (2014)

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

- [End-to-end Weakly-supervised Single-stage Multiple 3D Hand Mesh Reconstruction from a Single RGB Image](https://mlsanity.com/paper/2204.08154/) (2022)

## Abstract (arXiv)

The current state-of-the-art hand gesture recognition methodologies heavily rely in the use of machine learning. However there are scenarios that machine learning cannot be applied successfully, for example in situations where data is scarce. This is the case when one-to-one matching is required between a query and a dataset of hand gestures where each gesture represents a unique class. In situations where learning algorithms cannot be trained, classic computer vision techniques such as feature extraction can be used to identify similarities between objects. Shape is one of the most important features that can be extracted from images, however the most accurate shape matching algorithms tend to be computationally inefficient for real-time applications. In this work we present a novel shape matching methodology for real-time hand gesture recognition. Extensive experiments were carried out comparing our method with other shape matching methods with respect to accuracy and computational complexity using our own collected hand gesture dataset and a modified version of the MPEG-7 dataset.%that is widely used for comparing 2D shape matching algorithms. Our method outperforms the other methods and provides a good combination of accuracy and computational efficiency for real-time applications.