Of various Human-Computer-Interactions (HCI), hand gesture based HCI might be the most natural and intuitive way to communicate between people and machines, since it closely mimics how human interact with each other. Its intuitiveness and naturalness have spawned many applications in exploring large and complex data, computer games, virtual reality, health care, etc. Although the market for hand gesture based HCI is huge, building a robust hand gesture recognition system remains a challenging problem for traditional vision-based approaches, which are greatly limited by the quality of the input from optical sensors. [16] proposed a novel dissimilarity distance metric for hand gesture recognition using Kinect sensor, called Finger-Earth Mover's Distance (FEMD). In this paper, we compare the performance in terms of speed and accuracy between FEMD and traditional corresponding-based shape matching algorithm, Shape Context. And then we introduce several HCI applications built on top of a accurate and robust hand gesture recognition system based on FEMD. This hand gesture recognition system performs robustly despite variations in hand orientation, scale or articulation. Moreover, it works well in uncontrolled environments with background clusters. We demonstrate that this robust hand gesture recognition system can be a key enabler for numerous hand gesture based HCI systems.
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