2D Sensor Based Design of a Dynamic Hand Gesture Interpretation System

David Ciprian,Vasile Gui

doi:10.4028/www.scientific.net/aef.8-9.553

David Ciprian, Vasile Gui

Open Access

https://doi.org/10.4028/www.scientific.net/aef.8-9.553

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Abstract

A complete 2D sensor based system for dynamic gesture interpretation is presented in this paper. A hand model is devised for this purpose, composed of the palm area and the fingertips. Multiple cues are integrated in a feature space. Segmentation is carried out in this space to output the hand model. The robust technique of mean shift mode estimation is used to estimate the parameters of the hand model, making it adaptive and robust. The model is validated in various experiments concerning difficult situations like occlusion, varying illumination, and camouflage. Real time requirements are also met. The gesture interpretation approach refers to dynamic hand gestures. A collection of fingertip locations is collected from the hand model. Tensor voting approach is used to smooth and reconstruct the trajectory. The final output is represented by an encoding sequence of local trajectory directions. These are obtained by mean shift mode detection on the trajectory representation on Radon space. This module was tested and proved highly accurate.

Highlights

Human computer interfaces (HCIs) gained a steadily growing interest over the last decades
We have presented a new complete design for gesture recognition to be used in HCI applications
Our system is intended mainly for dynamic gestures. It is composed from a hand model extraction block, followed by gesture interpretation

Summary

Introduction

Human computer interfaces (HCIs) gained a steadily growing interest over the last decades. There are two different types of hand gesture based HCIs. One is focused on interpreting static hand poses and the other one is based on dynamic hand gestures. Our proposal refers to dynamic gestures HCIs and their applications. Various proposals [4, 5, 6] use 3D sensors for hand gesture recognition. Even if the extension of the approach to a 3D sensor use is trivial, we intend here to prove that an efficient design is possible using but a 2D sensor (for the sake of having a cheaper hardware implementation). The gesture recognition algorithm is to be designed to the used hand model. The gesture recognition stage has the task of extracting and labeling groups of features obtained from the hand model.

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