Literature on Hand GESTURE Recognition using Graph based methods

TL;DR

This paper reviews graph-based methods for hand gesture recognition, emphasizing skeleton point analysis, cloud mechanisms, neighbor retrieval, and the use of LSTM for capturing spatial-temporal information.

Contribution

It provides a comprehensive summary of graph-based hand gesture recognition techniques, highlighting innovations in point cloud handling and temporal modeling with LSTM.

Findings

Graph-based methods effectively capture spatial-temporal features.

Using neighbor retrieval reduces noise in point cloud data.

LSTM enhances long-term temporal modeling in gesture recognition.

Abstract

Skeleton based recognition systems are gaining popularity and machine learning models focusing on points or joints in a skeleton have proved to be computationally effective and application in many areas like Robotics. It is easy to track points and thereby preserving spatial and temporal information, which plays an important role in abstracting the required information, classification becomes an easy task. In this paper, we aim to study these points but using a cloud mechanism, where we define a cloud as collection of points. However, when we add temporal information, it may not be possible to retrieve the coordinates of a point in each frame and hence instead of focusing on a single point, we can use k-neighbors to retrieve the state of the point under discussion. Our focus is to gather such information using weight sharing but making sure that when we try to retrieve the information from neighbors, we do not carry noise with it. LSTM which has capability of long-term modelling and can carry both temporal and spatial information. In this article we tried to summarise graph based gesture recognition method.