Abstract
This paper proposes an approach to 2D gesture recognition that models each gesture as a Finite State Machine (FSM) in the spatial-temporal space. The model construction works in a semi-automatic way. The structure of the model is first manually decided based on the observation of the spatial topology of the data. The model is refined iteratively between two stages: data segmentation and model training. We incorporate a modified Knuth-Morris-Pratt algorithm into FSM recognition procedure to speed up the gesture recognition. The computational efficiency of the FSM recognizers allows real-time on-line performance to be achieved.