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

TL;DR

This paper introduces a new shape matching technique tailored for real-time hand gesture recognition, excelling in accuracy and efficiency, especially in scenarios with limited data where machine learning is unsuitable.

Contribution

The paper proposes a novel shape matching descriptor specifically designed for real-time hand gesture recognition, addressing computational efficiency and accuracy challenges.

Findings

Outperforms existing shape matching methods in accuracy

Offers a good balance of speed and precision for real-time use

Validated on custom and MPEG-7 datasets

Abstract

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.