1 research outputs found
A New GNG Graph-Based Hand Gesture Recognition Approach
Hand Gesture Recognition (HGR) is of major importance for Human-Computer
Interaction (HCI) applications. In this paper, we present a new hand gesture
recognition approach called GNG-IEMD. In this approach, first, we use a Growing
Neural Gas (GNG) graph to model the image. Then we extract features from this
graph. These features are not geometric or pixel-based, so do not depend on
scale, rotation, and articulation. The dissimilarity between hand gestures is
measured with a novel Improved Earth Mover\textquotesingle s Distance (IEMD)
metric. We evaluate the performance of the proposed approach on challenging
public datasets including NTU Hand Digits, HKU, HKU multi-angle, and UESTC-ASL
and compare the results with state-of-the-art approaches. The experimental
results demonstrate the performance of the proposed approach