Natural hand gestures for human identification in a Human-Computer Interface

TL;DR

This study explores using natural hand gestures as a biometric for human identification, demonstrating effective classification with various machine learning algorithms across different hardware setups.

Contribution

It formulates human identification via hand gestures as a classification problem and evaluates multiple classifiers and hardware types for effectiveness.

Findings

Hand gestures enable accurate human classification.

Support Vector Machines perform well in identification tasks.

Hardware type influences classification accuracy.

Abstract

The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.