Hand gesture recognition provides an attractive option for Human Computer Interaction (HCI). In particular, vision-based recognition of finger and hand gestures can help humans to communicate with a computer more efficiently. In this paper, we present a novel approach of recognizing finger and hand parts from a hand depth silhouette using Random Forests (RFs), a multi-class classifier, and its use for a hand gesture HCI. We present how to train the RFs using our own database. Then, the trained RFs are used to…
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