In this paper, we propose a real time 3D gesture recognition system that relies on the state based approach. The novelty of this work is the introduction of probabilistic neural networks (PNNs) to characterize the uncertain boundaries of each state. The 3D gestures are modeled as a sequence of states in a configuration space; the number of states and their spatial parameters are calculated by dynamic k-means clustering on the training data of the gesture without temporal information. Gesture recognition is performed using a simple Finite State Machine (FSM), where, each state transition depends only on the output of its corresponding PNN and optionally on its time restrictions (minimum and maximum time permitted in the state). If a recognizer reaches its final state, then it could be said that a gesture is recognized. The approach is illustrated with the implementation of a real time system that recognizes the semantic meaning of seven basic gestures of the Indian Dance, the description of the system and the technologies used, it will be described in detail in the paper.
['Otniel Portillo-Rodríguez', 'Oscar Osvaldo Sandoval-Gonzalez', 'Carlo Alberto Avizzano', 'Emanuele Ruffaldi', 'Davide Vercelli', 'Massimo Bergamasco']
Development of a 3D real time gesture recognition methodology for virtual environment control