Abstract

This paper presents a method for recognizing human-hand gestures using a model-based approach. A finite state machine is used to model four qualitatively distinct phases of a generic gesture. Fingertips are tracked in multiple frames to compute motion trajectories. The trajectories are then used for finding the start and stop position of the gesture. Gestures are represented as a list of vectors and are then matched to stored gesture vector models using table lookup based on vector displacements. Results are presented showing recognition of seven gestures using images sampled at 4Hz on a SPARC-1 without any special hardware. The seven gestures are representatives for actions of Left, Right, Up, Down, Grab, Rotate, and Stop. Keywords Motion Based Recognition, Motion Tracking, Gesture Interpretation. The research reported here was supported by the National Science Foundation grants CDA-9200369 and IRI-9220768. 1 1 Introduction It is essential for computer systems to possess the abi...

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