A New GNG Graph-Based Hand Gesture Recognition Approach
This addresses hand gesture recognition for human-computer interaction applications, presenting an incremental improvement over existing methods.
The paper tackles hand gesture recognition by proposing GNG-IEMD, which uses a Growing Neural Gas graph to model images and an Improved Earth Mover's Distance metric for dissimilarity measurement, achieving competitive results on multiple public datasets.
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.